text:
We first prove the objective function of RSM problem is not submodular. Then, we propose both submodular lower-bound and upper-bound of the objective function. Next, we develop a heuristic algorithm to approximate the objective function. Furthermore, we reformulate our objective function as the DS function (the Difference of Submodular functions). Finally, we conduct experiments on real-world datasets to evaluate our proposed method. The experiment results show that the upper and lower bounds are very close, which indicates the good quality of them. And, the proposed method outperforms the comparison methods.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {16},
numpages = {26},
keywords = {rumor blocking, Social network, non-submodularity, approximation algorithm}
}

@article{10.1145/3299876,
author = {Barton, Tomas and Bruna, Tomas and Kordik, Pavel},
title = {Chameleon 2: An Improved Graph-Based Clustering Algorithm},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3299876},
doi = {10.1145/3299876},
abstract = {Traditional clustering algorithms fail to produce human-like results when confronted with data of variable density, complex distributions, or in the presence of noise. We propose an improved graph-based clustering algorithm called Chameleon 2, which overcomes several drawbacks of state-of-the-art clustering approaches. We modified the internal cluster quality measure and added an extra step to ensure algorithm robustness. Our results reveal a significant positive impact on the clustering quality measured by Normalized Mutual Information on 32 artificial datasets used in the clustering literature. This significant improvement is also confirmed on real-world datasets.The performance of clustering algorithms such as DBSCAN is extremely parameter sensitive, and exhaustive manual parameter tuning is necessary to obtain a meaningful result. All hierarchical clustering methods are very sensitive to cutoff selection, and a human expert is often required to find the true cutoff for each clustering result. We present an automated cutoff selection method that enables the Chameleon 2 algorithm to generate high-quality clustering in autonomous mode.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {10},
numpages = {27},
keywords = {Cluster analysis, clustering, graph clustering, pattern recognition}
}

@article{10.1145/3298786,
author = {Leeuwen, Matthijs van and Chau, Polo and Vreeken, Jilles and Shahaf, Dafna and Faloutsos, Christos},
title = {Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3298786},
doi = {10.1145/3298786},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {13},
numpages = {2}
}

@article{10.1145/3299886,
author = {Wang, Huan and Wu, Jia and Hu, Wenbin and Wu, Xindong},
title = {Detecting and Assessing Anomalous Evolutionary Behaviors of Nodes in Evolving Social Networks},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3299886},
doi = {10.1145/3299886},
abstract = {Based on the performance of entire social networks, anomaly analysis for evolving social networks generally ignores the otherness of the evolutionary behaviors of different nodes, such that it is difficult to precisely identify the anomalous evolutionary behaviors of nodes (AEBN). Assuming that a node's evolutionary behavior that generates and removes edges normally follows stable evolutionary mechanisms, this study focuses on detecting and assessing AEBN, whose evolutionary mechanisms deviate from their past mechanisms, and proposes a link prediction detection (LPD) method and a matrix perturbation assessment (MPA) method. LPD describes a node's evolutionary behavior by fitting its evolutionary mechanism, and designs indexes for edge generation and removal to evaluate the extent to which the evolutionary mechanism of a node's evolutionary behavior can be fitted by a link prediction algorithm. Furthermore, it detects AEBN by quantifying the differences among behavior vectors that characterize the node's evolutionary behaviors in different periods. In addition, MPA considers AEBN as a perturbation of the social network structure, and quantifies the effect of AEBN on the social network structure based on matrix perturbation analysis. Extensive experiments on eight disparate real-world networks demonstrate that analyzing AEBN from the perspective of evolutionary mechanisms is important and beneficial.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {12},
numpages = {24},
keywords = {detection, Anomalous evolutionary behaviors of nodes, perturbation analysis, assessment, social network}
}

@article{10.1145/3299877,
author = {Murai, Fabricio and Ribeiro, Bruno and Towlsey, Don and Wang, Pinghui},
title = {Characterizing Directed and Undirected Networks via Multidimensional Walks with Jumps},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3299877},
doi = {10.1145/3299877},
abstract = {Estimating distributions of node characteristics (labels) such as number of connections or citizenship of users in a social network via edge and node sampling is a vital part of the study of complex networks. Due to its low cost, sampling via a random walk (RW) has been proposed as an attractive solution to this task. Most RW methods assume either that the network is undirected or that walkers can traverse edges regardless of their direction. Some RW methods have been designed for directed networks where edges coming into a node are not directly observable. In this work, we propose Directed Unbiased Frontier Sampling (DUFS), a sampling method based on a large number of coordinated walkers, each starting from a node chosen uniformly at random. It applies to directed networks with invisible incoming edges because it constructs, in real time, an undirected graph consistent with the walkers trajectories, and its use of random jumps to prevent walkers from being trapped. DUFS generalizes previous RW methods and is suited for undirected networks and to directed networks regardless of in-edge visibility. We also propose an improved estimator of node label distribution that combines information from initial walker locations with subsequent RW observations. We evaluate DUFS, compare it to other RW methods, investigate the impact of its parameters on estimation accuracy and provide practical guidelines for choosing them. In estimating out-degree distributions, DUFS yields significantly better estimates of the head of the distribution than other methods, while matching or exceeding estimation accuracy of the tail. Last, we show that DUFS outperforms uniform sampling when estimating distributions of node labels of the top 10% largest degree nodes, even when sampling a node uniformly has the same cost as RW steps.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {11},
numpages = {33},
keywords = {graph sampling, Complex networks, random walks, directed networks}
}

@article{10.1145/3299875,
author = {Sahoo, Doyen and Hoi, Steven C. H. and Li, Bin},
title = {Large Scale Online Multiple Kernel Regression with Application to Time-Series Prediction},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3299875},
doi = {10.1145/3299875},
abstract = {Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks as follows: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume that a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this work presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual/heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. We propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks including application to AR, ARMA, and ARIMA time series. We develop novel approaches to make OMKR scalable for large datasets, to counter the problems arising from an unbounded number of support vectors. We also explore the effect of kernel combination at prediction level and at the representation level. Finally, we conduct extensive experiments to evaluate the empirical performance on both real-world regression and times series prediction tasks.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {9},
numpages = {33},
keywords = {Online learning, multiple kernel regression, large-scale kernel learning, time-series prediction}
}

@article{10.1145/3278607,
author = {Song, Qingquan and Ge, Hancheng and Caverlee, James and Hu, Xia},
title = {Tensor Completion Algorithms in Big Data Analytics},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3278607},
doi = {10.1145/3278607},
abstract = {Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide attention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from the following four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature along with several available software repositories. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {6},
numpages = {48},
keywords = {Tensor, tensor completion, dynamic data analysis, multilinear data analysis, big data analytics, tensor factorization, tensor decomposition}
}

@article{10.1145/3285954,
author = {Chen, Hung-Hsuan and Chen, Pu},
title = {Differentiating Regularization Weights -- A Simple Mechanism to Alleviate Cold Start in Recommender Systems},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3285954},
doi = {10.1145/3285954},
abstract = {Matrix factorization (MF) and its extended methodologies have been studied extensively in the community of recommender systems in the last decade. Essentially, MF attempts to search for low-ranked matrices that can (1) best approximate the known rating scores, and (2) maintain low Frobenius norm for the low-ranked matrices to prevent overfitting. Since the two objectives conflict with each other, the common practice is to assign the relative importance weights as the hyper-parameters to these objectives. The two low-ranked matrices returned by MF are often interpreted as the latent factors of a user and the latent factors of an item that would affect the rating of the user on the item. As a result, it is typical that, in the loss function, we assign a regularization weight λp on the norms of the latent factors for all users, and another regularization weight λq on the norms of the latent factors for all the items. We argue that such a methodology probably over-simplifies the scenario. Alternatively, we probably should assign lower constraints to the latent factors associated with the items or users that reveal more information, and set higher constraints to the others. In this article, we systematically study this topic. We found that such a simple technique can improve the prediction results of the MF-based approaches based on several public datasets. Specifically, we applied the proposed methodology on three baseline models -- SVD, SVD++, and the NMF models. We found that this technique improves the prediction accuracy for all these baseline models. Perhaps more importantly, this technique better predicts the ratings on the long-tail items, i.e., the items that were rated/viewed/purchased by few users. This suggests that this approach may partially remedy the cold-start issue. The proposed method is very general and can be easily applied on various recommendation models, such as Factorization Machines, Field-aware Factorization Machines, Factorizing Personalized Markov Chains, Prod2Vec, Behavior2Vec, and so on. We release the code for reproducibility. We implemented a Python package that integrates the proposed regularization technique with the SVD, SVD++, and the NMF model. The package can be accessed at https://github.com/ncu-dart/rdf.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {8},
numpages = {22},
keywords = {SVD, collaborative filtering, cold start, matrix factorization, SVD++, long tail, Recommender systems}
}

@article{10.1145/3230666,
author = {Siddiqui, Md Amran and Fern, Alan and Dietterich, Thomas G. and Wong, Weng-Keen},
title = {Sequential Feature Explanations for Anomaly Detection},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3230666},
doi = {10.1145/3230666},
abstract = {In many applications, an anomaly detection system presents the most anomalous data instance to a human analyst, who then must determine whether the instance is truly of interest (e.g., a threat in a security setting). Unfortunately, most anomaly detectors provide no explanation about why an instance was considered anomalous, leaving the analyst with no guidance about where to begin the investigation. To address this issue, we study the problems of computing and evaluating sequential feature explanations (SFEs) for anomaly detectors. An SFE of an anomaly is a sequence of features, which are presented to the analyst one at a time (in order) until the information contained in the highlighted features is enough for the analyst to make a confident judgement about the anomaly. Since analyst effort is related to the amount of information that they consider in an investigation, an explanation’s quality is related to the number of features that must be revealed to attain confidence. In this article, we first formulate the problem of optimizing SFEs for a particular density-based anomaly detector. We then present both greedy algorithms and an optimal algorithm, based on branch-and-bound search, for optimizing SFEs. Finally, we provide a large scale quantitative evaluation of these algorithms using a novel framework for evaluating explanations. The results show that our algorithms are quite effective and that our best greedy algorithm is competitive with optimal solutions.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {1},
numpages = {22},
keywords = {anomaly explanation, explanation evaluation, anomaly interpretation, Anomaly detection}
}

@article{10.1145/3278606,
author = {Tao, Zhiqiang and Liu, Hongfu and Li, Sheng and Ding, Zhengming and Fu, Yun},
title = {Robust Spectral Ensemble Clustering via Rank Minimization},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3278606},
doi = {10.1145/3278606},
abstract = {Ensemble Clustering (EC) is an important topic for data cluster analysis. It targets to integrate multiple Basic Partitions (BPs) of a particular dataset into a consensus partition. Among previous works, one promising and effective way is to transform EC as a graph partitioning problem on the co-association matrix, which is a pair-wise similarity matrix summarized by all the BPs in essence. However, most existing EC methods directly utilize the co-association matrix, yet without considering various noises (e.g., the disagreement between different BPs and the outliers) that may exist in it. These noises can impair the cluster structure of a co-association matrix, and thus mislead the final graph partitioning process. To address this challenge, we propose a novel Robust Spectral Ensemble Clustering (RSEC) algorithm in this article. Specifically, we learn low-rank representation (LRR) for the co-association matrix to uncover its cluster structure and handle the noises, and meanwhile, we perform spectral clustering with the learned representation to seek for a consensus partition. These two steps are jointly proceeded within a unified optimization framework. In particular, during the optimizing process, we leverage consensus partition to iteratively enhance the block-diagonal structure of LRR, in order to assist the graph partitioning. To solve RSEC, we first formulate it by using nuclear norm as a convex proxy to the rank function. Then, motivated by the recent advances in non-convex rank minimization, we further develop a non-convex model for RSEC and provide it a solution by the majorization--minimization Augmented Lagrange Multiplier algorithm. Experiments on 18 real-world datasets demonstrate the effectiveness of our algorithm compared with state-of-the-art methods. Moreover, several impact factors on the clustering performance of our approach are also explored extensively.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {4},
numpages = {25},
keywords = {co-association matrix, non-convex relaxation, Ensemble clustering, low-rank representation, spectral clustering}
}

@article{10.1145/3281631,
author = {Moghaz, Dror and Hacohen-Kerner, Yaakov and Gabbay, Dov},
title = {Text Mining for Evaluating Authors' Birth and Death Years},
year = {2019},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3281631},
doi = {10.1145/3281631},
abstract = {This article presents a unique method in text and data mining for finding the era, i.e., mining temporal data, in which an anonymous author was living. Finding this era can assist in the examination of a fake document or extracting the time period in which a writer lived. The study and the experiments concern Hebrew, and in some parts, Aramaic and Yiddish rabbinic texts. The rabbinic texts are undated and contain no bibliographic sections, posing an interesting challenge. This work proposes algorithms using key phrases and key words that allow the temporal organization of citations together with linguistic patterns. Based on these key phrases, key words, and the references, we established several types of “Iron-clad,” Heuristic and Greedy rules for estimating the years of birth and death of a writer in an interesting classification task. Experiments were conducted on corpora, including documents authored by 12, 24, and 36 rabbinic writers and demonstrated promising results.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {7},
numpages = {24},
keywords = {undated documents, knowledge discovery, time analysis, key-phrases, text and data mining, Hebrew-Aramaic documents, Temporal-data}
}

@article{10.1145/3274670,
author = {Lagr\'{e}e, Paul and Capp\'{e}, Olivier and Cautis, Bogdan and Maniu, Silviu},
title = {Algorithms for Online Influencer Marketing},
year = {2018},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3274670},
doi = {10.1145/3274670},
abstract = {Influence maximization is the problem of finding influential users, or nodes, in a graph so as to maximize the spread of information. It has many applications in advertising and marketing on social networks. In this article, we study a highly generic version of influence maximization, one of optimizing influence campaigns by sequentially selecting “spread seeds” from a set of influencers, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain persistently active. This problem is in particular relevant for an important form of online marketing, known as influencer marketing, in which the marketers target a sub-population of influential people, instead of the entire base of potential buyers. Importantly, we make no assumptions on the underlying diffusion model, and we work in a setting where neither a diffusion network nor historical activation data are available. We call this problem online influencer marketing with persistence (in short, OIMP). We first discuss motivating scenarios and present our general approach. We introduce an estimator on the influencers’ remaining potential – the expected number of nodes that can still be reached from a given influencer – and justify its strength to rapidly estimate the desired value, relying on real data gathered from Twitter. We then describe a novel algorithm, GT-UCB, relying on probabilistic upper confidence bounds on the remaining potential. We show that our approach leads to high-quality spreads on both simulated and real datasets. Importantly, it is orders of magnitude faster than state-of-the-art influence maximization methods, making it possible to deal with large-scale online scenarios.},
journal = {ACM Trans. Knowl. Discov. Data},
month = dec,
articleno = {3},
numpages = {30},
keywords = {online learning, information diffusion, influence maximization, Influencer marketing, multi-armed bandits, online social networks}
}

@article{10.1145/3281632,
author = {Jaysawal, Bijay Prasad and Huang, Jen-Wei},
title = {PSP-AMS: Progressive Mining of Sequential Patterns Across Multiple Streams},
year = {2018},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3281632},
doi = {10.1145/3281632},
abstract = {Sequential pattern mining is used to find frequent data sequences over time. When sequential patterns are generated, the newly arriving patterns may not be identified as frequent sequential patterns due to the existence of old data and sequences. Progressive sequential pattern mining aims to find the most up-to-date sequential patterns given that obsolete items will be deleted from the sequences. When sequences come with multiple data streams, it is difficult to maintain and update the current sequential patterns. Even worse, when we consider the sequences across multiple streams, previous methods cannot efficiently compute the frequent sequential patterns. In this work, we propose an efficient algorithm PSP-AMS to address this problem. PSP-AMS uses a novel data structure PSP-MS-tree to insert new items, update current items, and delete obsolete items. By maintaining a PSP-MS-tree, PSP-AMS efficiently finds the frequent sequential patterns across multiple streams. The experimental results show that PSP-AMS significantly outperforms previous algorithms for mining of progressive sequential patterns across multiple streams on synthetic data as well as real data.},
journal = {ACM Trans. Knowl. Discov. Data},
month = dec,
articleno = {5},
numpages = {23},
keywords = {across-streams sequential patterns, multiple data streams, Progressive mining, sequential patterns, across data streams}
}

@article{10.1145/3267106,
author = {Liu, Xiaoming and Shen, Chao and Guan, Xiaohong and Zhou, Yadong},
title = {D<span class="smallcaps SmallerCapital">igger</span>: Detect Similar Groups in Heterogeneous Social Networks},
year = {2018},
issue_date = {January 2019},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {13},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3267106},
doi = {10.1145/3267106},
abstract = {People participate in multiple online social networks, e.g., Facebook, Twitter, and Linkedin, and these social networks with heterogeneous social content and user relationship are named as heterogeneous social networks. Group structure widely exists in heterogeneous social networks, which reveals the evolution of human cooperation. Detecting similar groups in heterogeneous networks has a great significance for many applications, such as recommendation system and spammer detection, using the wealth of group information. Although promising, this novel problem encounters a variety of technical challenges, including incomplete data, high time complexity, and ground truth. To address the research gap and technical challenges, we take advantage of a ratio-cut optimization function to model this novel problem by the linear mixed-effects method and graph spectral theory. Based on this model, we propose an efficient algorithm called Digger to detect the similar groups in the large graphs. Digger consists of three steps, including measuring user similarity, construct a matching graph, and detecting similar groups. We adopt several strategies to lower the computational cost and detail the basis of labeling the ground truth. We evaluate the effectiveness and efficiency of our algorithm on five different types of online social networks. The extensive experiments show that our method achieves 0.693, 0.783, and 0.735 in precision, recall, and F1-measure, which significantly surpass the state-of-arts by 24.4%, 15.3%, and 20.7%, respectively. The results demonstrate that our proposal can detect similar groups in heterogeneous networks effectively.},
journal = {ACM Trans. Knowl. Discov. Data},
month = dec,
articleno = {2},
numpages = {27},
keywords = {graph spectral, linear mixed-effects, Heterogeneous networks, detecting similar groups}
}

@article{10.1145/3184455,
author = {Wen, Xidao and Lin, Yu-Ru and Pelechrinis, Konstantinos},
title = {Event Analytics via Discriminant Tensor Factorization},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3184455},
doi = {10.1145/3184455},
abstract = {Analyzing the impact of disastrous events has been central to understanding and responding to crises. Traditionally, the assessment of disaster impact has primarily relied on the manual collection and analysis of surveys and questionnaires as well as the review of authority reports. This can be costly and time-consuming, whereas a timely assessment of an event’s impact is critical for crisis management and humanitarian operations. In this work, we formulate the impact discovery as the problem to identify the shared and discriminative subspace via tensor factorization due to the multi-dimensional nature of mobility data. Existing work in mining the shared and discriminative subspaces typically requires the predefined number of either type of them. In the context of event impact discovery, this could be impractical, especially for those unprecedented events. To overcome this, we propose a new framework, called “PairFac,” that jointly factorizes the multi-dimensional data to discover the latent mobility pattern along with its associated discriminative weight. This framework does not require splitting the shared and discriminative subspaces in advance and at the same time automatically captures the persistent and changing patterns from multi-dimensional behavioral data. Our work has important applications in crisis management and urban planning, which provides a timely assessment of impacts of major events in the urban environment.},
journal = {ACM Trans. Knowl. Discov. Data},
month = oct,
articleno = {72},
numpages = {38},
keywords = {Urban computing, event analytics, data mining, tensor factorization}
}

@article{10.1145/3264745,
author = {Chen, Chaochao and Chang, Kevin Chen-Chuan and Li, Qibing and Zheng, Xiaolin},
title = {Semi-Supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3264745},
doi = {10.1145/3264745},
abstract = {Recently, latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this article, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.},
journal = {ACM Trans. Knowl. Discov. Data},
month = oct,
articleno = {73},
numpages = {24},
keywords = {latent factor model, chain graph model, data sparsity, Semi-supervised learning}
}

@article{10.1145/3237191,
author = {Park, Ha-Myung and Silvestri, Francesco and Pagh, Rasmus and Chung, Chin-Wan and Myaeng, Sung-Hyon and Kang, U},
title = {Enumerating Trillion Subgraphs On Distributed Systems},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3237191},
doi = {10.1145/3237191},
abstract = {How can we find patterns from an enormous graph with billions of vertices and edges? The subgraph enumeration, which is to find patterns from a graph, is an important task for graph data analysis with many applications, including analyzing the social network evolution, measuring the significance of motifs in biological networks, observing the dynamics of Internet, and so on. Especially, the triangle enumeration, a special case of the subgraph enumeration, where the pattern is a triangle, has many applications such as identifying suspicious users in social networks, detecting web spams, and finding communities. However, recent networks are so large that most of the previous algorithms fail to process them. Recently, several MapReduce algorithms have been proposed to address such large networks; however, they suffer from the massive shuffled data resulting in a very long processing time.In this article, we propose scalable methods for enumerating trillion subgraphs on distributed systems. We first propose PTE (Pre-partitioned Triangle Enumeration), a new distributed algorithm for enumerating triangles in enormous graphs by resolving the structural inefficiency of the previous MapReduce algorithms. PTE enumerates trillions of triangles in a billion scale graph by decreasing three factors: the amount of shuffled data, total work, and network read. We also propose PSE (Pre-partitioned Subgraph Enumeration), a generalized version of PTE for enumerating subgraphs that match an arbitrary query graph. Experimental results show that PTE provides 79 times faster performance than recent distributed algorithms on real-world graphs, and succeeds in enumerating more than 3 trillion triangles on the ClueWeb12 graph with 6.3 billion vertices and 72 billion edges. Furthermore, PSE successfully enumerates 265 trillion clique subgraphs with 4 vertices from a subdomain hyperlink network, showing 47 times faster performance than the state of the art distributed subgraph enumeration algorithm.},
journal = {ACM Trans. Knowl. Discov. Data},
month = oct,
articleno = {71},
numpages = {30},
keywords = {scalable algorithm, Triangle enumeration, graph algorithm, network analysis, distributed algorithm, big data, subgraph enumeration}
}

@article{10.1145/3241063,
author = {Huang, Xiao and Li, Jundong and Zou, Na and Hu, Xia},
title = {A General Embedding Framework for Heterogeneous Information Learning in Large-Scale Networks},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3241063},
doi = {10.1145/3241063},
abstract = {Network analysis has been widely applied in many real-world tasks, such as gene analysis and targeted marketing. To extract effective features for these analysis tasks, network embedding automatically learns a low-dimensional vector representation for each node, such that the meaningful topological proximity is well preserved. While the embedding algorithms on pure topological structure have attracted considerable attention, in practice, nodes are often abundantly accompanied with other types of meaningful information, such as node attributes, second-order proximity, and link directionality. A general framework for incorporating the heterogeneous information into network embedding could be potentially helpful in learning better vector representations. However, it remains a challenging task to jointly embed the geometrical structure and a distinct type of information due to the heterogeneity. In addition, the real-world networks often contain a large number of nodes, which put demands on the scalability of the embedding algorithms. To bridge the gap, in this article, we propose a general embedding framework named Heterogeneous Information Learning in Large-scale networks (HILL) to accelerate the joint learning. It enables the simultaneous node proximity assessing process to be done in a distributed manner by decomposing the complex modeling and optimization into many simple and independent sub-problems. We validate the significant correlation between the heterogeneous information and topological structure, and illustrate the generalizability of HILL by applying it to perform attributed network embedding and second-order proximity learning. A variation is proposed for link directionality modeling. Experimental results on real-world networks demonstrate the effectiveness and efficiency of HILL.},
journal = {ACM Trans. Knowl. Discov. Data},
month = oct,
articleno = {70},
numpages = {24},
keywords = {Data mining, distributed processing, heterogeneity, network embedding}
}

@article{10.1145/3233227,
author = {He, Xinran and Kempe, David},
title = {Stability and Robustness in Influence Maximization},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3233227},
doi = {10.1145/3233227},
abstract = {In the well-studied Influence Maximization problem, the goal is to identify a set of k nodes in a social network whose joint influence on the network is maximized. A large body of recent work has justified research on Influence Maximization models and algorithms with their potential to create societal or economic value. However, in order to live up to this potential, the algorithms must be robust to large amounts of noise, for they require quantitative estimates of the influence, which individuals exert on each other; ground truth for such quantities is inaccessible, and even decent estimates are very difficult to obtain.We begin to address this concern formally. First, we exhibit simple inputs on which even very small estimation errors may mislead every algorithm into highly suboptimal solutions. Motivated by this observation, we propose the Perturbation Interval model as a framework to characterize the stability of Influence Maximization against noise in the inferred diffusion network. Analyzing the susceptibility of specific instances to estimation errors leads to a clean algorithmic question, which we term the Influence Difference Maximization problem. However, the objective function of Influence Difference Maximization is NP-hard to approximate within a factor of O(n1−ϵ) for any ϵ > 0.Given the infeasibility of diagnosing instability algorithmically, we focus on finding influential users robustly across multiple diffusion settings. We define a Robust Influence Maximization framework wherein an algorithm is presented with a set of influence functions. The algorithm’s goal is to identify a set of k nodes who are simultaneously influential for all influence functions, compared to the (function-specific) optimum solutions. We show strong approximation hardness results for this problem unless the algorithm gets to select at least a logarithmic factor more seeds than the optimum solution. However, when enough extra seeds may be selected, we show that techniques of Krause et al. can be used to approximate the optimum robust influence to within a factor of 1−1/e. We evaluate this bicriteria approximation algorithm against natural heuristics on several real-world datasets. Our experiments indicate that the worst-case hardness does not necessarily translate into bad performance on real-world datasets; all algorithms perform fairly well.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {66},
numpages = {34},
keywords = {Social networks, robust optimization, stability analysis, influence maximization, submodular optimization, information diffusion}
}

@article{10.1145/3230668,
author = {Liu, Xiaoli and Cao, Peng and Gon\c{c}alves, Andr\'{e} R. and Zhao, Dazhe and Banerjee, Arindam},
title = {Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3230668},
doi = {10.1145/3230668},
abstract = {Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is thus an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. In this article, we propose the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the proposed model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {65},
numpages = {35},
keywords = {multi-task learning, Alzheimer’s disease, ADMM, graph laplacian, disease progression}
}

@article{10.1145/3234943,
author = {Ying, Josh Jia-Ching and Zhang, Ji and Huang, Che-Wei and Chen, Kuan-Ta and Tseng, Vincent S.},
title = {<i>FrauDetector</i><sup>+</sup>: An Incremental Graph-Mining Approach for Efficient Fraudulent Phone Call Detection},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3234943},
doi = {10.1145/3234943},
abstract = {In recent years, telecommunication fraud has become more rampant internationally with the development of modern technology and global communication. Because of rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with big data issues in real-world implementations. Although our previous work, FrauDetector, addressed this problem and achieved some promising results, it can be further enhanced because it focuses only on fraud detection accuracy, whereas the efficiency and scalability are not top priorities. Other known approaches for fraudulent call number detection suffer from long training times or cannot accurately detect fraudulent phone calls in real time. However, the learning process of FrauDetector is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of FrauDetector by parallelization, the parallelized learning process, namely PFrauDetector, still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetector+, which can automatically label fraudulent phone numbers with a “fraud” tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones. FrauDetector+ initially generates smaller, more manageable subnetworks from original graph and performs a parallelized weighted HITS algorithm for a significant speed increase in the graph learning module. It adopts a novel aggregation approach to generate a trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an antifraud mobile application called Whoscall. The results demonstrate a significantly improved efficiency of our approach compared with FrauDetector as well as superior performance against other major classifier-based methods.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {68},
numpages = {35},
keywords = {incremental learning, Telecommunication fraud, trust value mining, parallelized weighted HITS algorithm, fraudulent phone call detection}
}

@article{10.1145/3230967,
author = {Wang, Can and Chi, Chi-Hung and She, Zhong and Cao, Longbing and Stantic, Bela},
title = {Coupled Clustering Ensemble by Exploring Data Interdependence},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3230967},
doi = {10.1145/3230967},
abstract = {Clustering ensembles combine multiple partitions of data into a single clustering solution. It is an effective technique for improving the quality of clustering results. Current clustering ensemble algorithms are usually built on the pairwise agreements between clusterings that focus on the similarity via consensus functions, between data objects that induce similarity measures from partitions and re-cluster objects, and between clusters that collapse groups of clusters into meta-clusters. In most of those models, there is a strong assumption on IIDness (i.e., independent and identical distribution), which states that base clusterings perform independently of one another and all objects are also independent. In the real world, however, objects are generally likely related to each other through features that are either explicit or even implicit. There is also latent but definite relationship among intermediate base clusterings because they are derived from the same set of data. All these demand a further investigation of clustering ensembles that explores the interdependence characteristics of data. To solve this problem, a new coupled clustering ensemble (CCE) framework that works on the interdependence nature of objects and intermediate base clusterings is proposed in this article. The main idea is to model the coupling relationship between objects by aggregating the similarity of base clusterings, and the interactive relationship among objects by addressing their neighborhood domains. Once these interdependence relationships are discovered, they will act as critical supplements to clustering ensembles. We verified our proposed framework by using three types of consensus function: clustering-based, object-based, and cluster-based. Substantial experiments on multiple synthetic and real-life benchmark datasets indicate that CCE can effectively capture the implicit interdependence relationships among base clusterings and among objects with higher clustering accuracy, stability, and robustness compared to 14 state-of-the-art techniques, supported by statistical analysis. In addition, we show that the final clustering quality is dependent on the data characteristics (e.g., quality and consistency) of base clusterings in terms of sensitivity analysis. Finally, the applications in document clustering, as well as on the datasets with much larger size and dimensionality, further demonstrate the effectiveness, efficiency, and scalability of our proposed models.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {63},
numpages = {38},
keywords = {interdependence, behavior interior dimensions, object, Clustering ensemble, coupling, base clustering}
}

@article{10.1145/3233185,
author = {Romero-Tris, Cristina and Meg\'{\i}as, David},
title = {Protecting Privacy in Trajectories with a User-Centric Approach},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3233185},
doi = {10.1145/3233185},
abstract = {The increased use of location-aware devices, such as smartphones, generates a large amount of trajectory data. These data can be useful in several domains, like marketing, path modeling, localization of an epidemic focus, and so on. Nevertheless, since trajectory information contains personal mobility data, improper use or publication of trajectory data can threaten users’ privacy. It may reveal sensitive details like habits of behavior, religious beliefs, and sexual preferences. Therefore, many users might be unwilling to share their trajectory data without a previous anonymization process. Currently, several proposals to address this problem can be found in the literature. These solutions focus on anonymizing data before its publication, i.e., when they are already stored in the server database. Nevertheless, we argue that this approach gives the user no control about the information she shares. For this reason, we propose anonymizing data in the users’ mobile devices, before they are sent to a third party. This article extends our previous work which was, to the best of our knowledge, the first one to anonymize data at the client side, allowing users to select the amount and accuracy of shared data. In this article, we describe an improved version of the protocol, and we include the implementation together with an analysis of the results obtained after the simulation with real trajectory data.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {67},
numpages = {27},
keywords = {Trajectory anonymization, user-centric protocol, privacy}
}

@article{10.1145/3230667,
author = {Di, Mingyang and Klabjan, Diego and Sha, Long and Lucey, Patrick},
title = {Large-Scale Adversarial Sports Play Retrieval with Learning to Rank},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3230667},
doi = {10.1145/3230667},
abstract = {As teams of professional leagues are becoming more and more analytically driven, the interest in effective data management and access of sports plays has dramatically increased. In this article, we present a retrieval system that can quickly find the most relevant plays from historical games given an input query. To search through a large number of games at an interactive speed, our system is built upon a distributed framework so that each query-result pair is evaluated in parallel. We also propose a pairwise learning to rank approach to improve search ranking based on users’ clickthrough behavior. The similarity metric in training the rank function is based on automatically learnt features from a convolutional autoencoder. Finally, we showcase the efficacy of our learning to rank approach by demonstrating rank quality in a user study.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {69},
numpages = {18},
keywords = {convolutional autoencoder, Similarity measures, learning to rank}
}

@article{10.1145/3233186,
author = {Huang, Zhipeng and Cautis, Bogdan and Cheng, Reynold and Zheng, Yudian and Mamoulis, Nikos and Yan, Jing},
title = {Entity-Based Query Recommendation for Long-Tail Queries},
year = {2018},
issue_date = {October 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {6},
issn = {1556-4681},
url = {https://doi.org/10.1145/3233186},
doi = {10.1145/3233186},
abstract = {Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.},
journal = {ACM Trans. Knowl. Discov. Data},
month = aug,
articleno = {64},
numpages = {24},
keywords = {entity, Query recommendation, knowledge base}
}

@article{10.1145/3201603,
author = {Santos, Ludovic Dos and Piwowarski, Benjamin and Denoyer, Ludovic and Gallinari, Patrick},
title = {Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201603},
doi = {10.1145/3201603},
abstract = {We address the task of node classification in heterogeneous networks, where the nodes are of different types, each type having its own set of labels, and the relations between nodes may also be of different types. A typical example is provided by social networks where node types may for example be users, content, or films, and relations friendship, like, authorship. Learning and performing inference on such heterogeneous networks is a recent task requiring new models and algorithms. We propose a model, Labeling Heterogeneous Network (LaHNet), a transductive approach to classification that learns to project the different types of nodes into a common latent space. This embedding is learned so as to reflect different characteristics of the problem such as the correlation between node labels, as well as the graph topology. The application focus is on social graphs, but the algorithm is general and can be used for other domains. The model is evaluated on five datasets representative of different instances of social data.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jul,
articleno = {62},
numpages = {33},
keywords = {node classification, relational data, Representation learning}
}

@article{10.1145/3208351,
author = {Riondato, Matteo and Upfal, Eli},
title = {ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3208351},
doi = {10.1145/3208351},
abstract = {ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning.We present ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, ABRA is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime number of samples, and accuracy, state-of-the-art algorithms with the same quality guarantees.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jul,
articleno = {61},
numpages = {38},
keywords = {pseudodimension, statistical learning theory, Centrality measures, uniform bounds}
}

@article{10.1145/3182382,
author = {Lines, Jason and Taylor, Sarah and Bagnall, Anthony},
title = {Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3182382},
doi = {10.1145/3182382},
abstract = {A recent experimental evaluation assessed 19 time series classification (TSC) algorithms and found that one was significantly more accurate than all others: the Flat Collective of Transformation-based Ensembles (Flat-COTE). Flat-COTE is an ensemble that combines 35 classifiers over four data representations. However, while comprehensive, the evaluation did not consider deep learning approaches. Convolutional neural networks (CNN) have seen a surge in popularity and are now state of the art in many fields and raises the question of whether CNNs could be equally transformative for TSC.We implement a benchmark CNN for TSC using a common structure and use results from a TSC-specific CNN from the literature. We compare both to Flat-COTE and find that the collective is significantly more accurate than both CNNs. These results are impressive, but Flat-COTE is not without deficiencies. We significantly improve the collective by proposing a new hierarchical structure with probabilistic voting, defining and including two novel ensemble classifiers built in existing feature spaces, and adding further modules to represent two additional transformation domains. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE), encapsulates classifiers built on five data representations. We demonstrate that HIVE-COTE is significantly more accurate than Flat-COTE (and all other TSC algorithms that we are aware of) over 100 resamples of 85 TSC problems and is the new state of the art for TSC. Further analysis is included through the introduction and evaluation of 3 new case studies and extensive experimentation on 1,000 simulated datasets of 5 different types.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jul,
articleno = {52},
numpages = {35},
keywords = {heterogeneous ensembles, Time series classification, meta ensembles, deep learning}
}

@article{10.1145/3201578,
author = {Bollegala, Danushka and Atanasov, Vincent and Maehara, Takanori and Kawarabayashi, Ken-Ichi},
title = {ClassiNet -- Predicting Missing Features for Short-Text Classification},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201578},
doi = {10.1145/3201578},
abstract = {Short and sparse texts such as tweets, search engine snippets, product reviews, and chat messages are abundant on the Web. Classifying such short-texts into a pre-defined set of categories is a common problem that arises in various contexts, such as sentiment classification, spam detection, and information recommendation. The fundamental problem in short-text classification is feature sparseness -- the lack of feature overlap between a trained model and a test instance to be classified. We propose ClassiNet -- a network of classifiers trained for predicting missing features in a given instance, to overcome the feature sparseness problem. Using a set of unlabeled training instances, we first learn binary classifiers as feature predictors for predicting whether a particular feature occurs in a given instance. Next, each feature predictor is represented as a vertex vi in the ClassiNet, where a one-to-one correspondence exists between feature predictors and vertices. The weight of the directed edge eij connecting a vertex vi to a vertex vj represents the conditional probability that given vi exists in an instance, vj also exists in the same instance.We show that ClassiNets generalize word co-occurrence graphs by considering implicit co-occurrences between features. We extract numerous features from the trained ClassiNet to overcome feature sparseness. In particular, for a given instance x, we find similar features from ClassiNet that did not appear in x, and append those features in the representation of x. Moreover, we propose a method based on graph propagation to find features that are indirectly related to a given short-text. We evaluate ClassiNets on several benchmark datasets for short-text classification. Our experimental results show that by using ClassiNet, we can statistically significantly improve the accuracy in short-text classification tasks, without having to use any external resources such as thesauri for finding related features.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {55},
numpages = {29},
keywords = {feature sparseness, short-texts, Classifier networks, text classification}
}

@article{10.1145/3201408,
author = {Zhang, Ziqi and Gao, Jie and Ciravegna, Fabio},
title = {SemRe-Rank: Improving Automatic Term Extraction by Incorporating Semantic Relatedness with Personalised PageRank},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201408},
doi = {10.1145/3201408},
abstract = {Automatic Term Extraction (ATE) deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that there is no existing ATE methods that can consistently outperform others in any domain. This work adopts a refreshed perspective to this problem: instead of searching for such a ‘one-size-fit-all’ solution that may never exist, we propose to develop generic methods to ‘enhance’ existing ATE methods. We introduce SemRe-Rank, the first method based on this principle, to incorporate semantic relatedness—an often overlooked venue—into an existing ATE method to further improve its performance. SemRe-Rank incorporates word embeddings into a personalised PageRank process to compute ‘semantic importance’ scores for candidate terms from a graph of semantically related words (nodes), which are then used to revise the scores of candidate terms computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all base methods and across all datasets, with up to 15 percentage points when measured by the Precision in the top ranked K candidate terms (the average for a set of K’s), or up to 28 percentage points in F1 measured at a K that equals to the expected real terms in the candidates (F1 in short). Compared to an alternative approach built on the well-known TextRank algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision at top K, or up to 17 points in F1.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {57},
numpages = {41},
keywords = {Automatic term extraction, information retrieval, word embedding, information extraction, text mining, termhood, ATR, semantic relatedness, personalised pagerank, automatic term recognition, ATE}
}

@article{10.1145/3201407,
author = {Li, Peipei and Wang, Haixun and Li, Hongsong and Wu, Xindong},
title = {Employing Semantic Context for Sparse Information Extraction Assessment},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201407},
doi = {10.1145/3201407},
abstract = {A huge amount of texts available on the World Wide Web presents an unprecedented opportunity for information extraction (IE). One important assumption in IE is that frequent extractions are more likely to be correct. Sparse IE is hence a challenging task because no matter how big a corpus is, there are extractions supported by only a small amount of evidence in the corpus. However, there is limited research on sparse IE, especially in the assessment of the validity of sparse IEs. Motivated by this, we introduce a lightweight, explicit semantic approach for assessing sparse IE.1 We first use a large semantic network consisting of millions of concepts, entities, and attributes to explicitly model the context of any semantic relationship. Second, we learn from three semantic contexts using different base classifiers to select an optimal classification model for assessing sparse extractions. Finally, experiments show that as compared with several state-of-the-art approaches, our approach can significantly improve the F-score in the assessment of sparse extractions while maintaining the efficiency.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {54},
numpages = {36},
keywords = {semantic network, isA relationship, classification, Sparse information extraction}
}

@article{10.1145/3209669,
author = {Silva, Fabr\'{\i}cio A. and Domingues, Augusto C. S. A. and Silva, Thais R. M. Braga},
title = {Discovering Mobile Application Usage Patterns from a Large-Scale Dataset},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3209669},
doi = {10.1145/3209669},
abstract = {The discovering of patterns regarding how, when, and where users interact with mobile applications reveals important insights for mobile service providers. In this work, we exploit for the first time a real and large-scale dataset representing the records of mobile application usage of 5,342 users during 2014. The data was collected by a software agent, installed at the users’ smartphones, which monitors detailed usage of applications. First, we look for general patterns of how users access some of the most popular mobile applications in terms of frequency, duration, diversity, and data traffic. Next, we mine the dataset looking for temporal patterns in terms of when and how often accesses occur. Finally, we exploit the location of each access to detect users’ points of interest and location-based communities. Based on the results, we derive a model to generate synthetic datasets of mobile application usage and evaluate solutions to predict the next application to be launched. We also discuss a series of implications of the findings regarding telecommunication services, mobile advertisements, and smart cities. This is the first time this dataset is used, and we also make it publicly available for other researchers.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {59},
numpages = {36},
keywords = {pattern mining, synthetic data generation, Mobile data}
}

@article{10.1145/3201604,
author = {Hao, Shuji and Hu, Peiying and Zhao, Peilin and Hoi, Steven C. H. and Miao, Chunyan},
title = {Online Active Learning with Expert Advice},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201604},
doi = {10.1145/3201604},
abstract = {In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an instance is disclosed only when it is requested by the proposed active query strategies. Our goal is to minimize the number of requests while training an online learning model without sacrificing the performance. To address this challenge, we propose a framework of active forecasters, which attempts to extend two fully supervised forecasters, Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of online active learning (OAL) with expert advice. Specifically, we proposed two OAL with expert advice algorithms, named Active Exponentially Weighted Average Forecaster (AEWAF) and active greedy forecaster (AGF), by considering the difference of expert advices. To further improve the robustness of the proposed AEWAF and AGF algorithms in the noisy scenarios (where noisy experts exist), we also proposed two robust active learning with expert advice algorithms, named Robust Active Exponentially Weighted Average Forecaster and Robust Active Greedy Forecaster. We validate the efficacy of the proposed algorithms by an extensive set of experiments in both normal scenarios (where all of experts are comparably reliable) and noisy scenarios.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {58},
numpages = {22},
keywords = {expert advice, data streaming, Online learning, active learning}
}

@article{10.1145/3211872,
author = {Li, Feijiang and Qian, Yuhua and Wang, Jieting and Dang, Chuangyin and Liu, Bing},
title = {Cluster’s Quality Evaluation and Selective Clustering Ensemble},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3211872},
doi = {10.1145/3211872},
abstract = {Clustering ensemble has drawn much attention in recent years due to its ability to generate a high quality and robust partition result. Weighted clustering ensemble and selective clustering ensemble are two general ways to further improve the performance of a clustering ensemble method. Existing weighted clustering ensemble methods assign the same weight to each cluster in a partition of the ensemble. Since the qualities of the clusters in a partition are different, the clusters should be weighted differently. To address this issue, this article proposes a new measure to calculate the similarity between a cluster and a partition. Theoretically, this measure is effective in handling two problems in measuring the quality of a cluster, which are defined as the symmetric problem and the context meaning problem. In addition, some properties of the proposed measure are analyzed. This measure can be easily expanded to a clustering performance measure that calculates the similarity between two partitions. As a result of this measure, we propose a novel selective clustering ensemble framework, which considers the differences between the objective of the ensemble selection stage and the object of the ensemble integration stage in the selective clustering ensemble. To verify the performance of the new measure, we compare the performance of the measure with the two existing measures in weighting clusters. The experiments show that the proposed measure is more effective. To verify the performance of the novel framework, four existing state-of-the-art selective clustering ensemble frameworks are employed as references. The experiments show that the proposed framework is statistically better than the others on 17 UCI benchmark datasets, 8 document datasets, and the Olivetti Face Database.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {60},
numpages = {27},
keywords = {selective clustering ensemble, weighted clustering ensemble, Clustering ensemble, cluster quality}
}

@article{10.1145/3201577,
author = {Qin, Tian and Shangguan, Wufan and Song, Guojie and Tang, Jie},
title = {Spatio-Temporal Routine Mining on Mobile Phone Data},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201577},
doi = {10.1145/3201577},
abstract = {Mining human behaviors has always been an important subarea of Data Mining. While it provides empirical evidences to psychological/behavioral studies, it also builds the foundation of various big-data systems, which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily commute behaviors of mobile subscribers and further conduct routine mining on them. In this article, we propose to model mobile subscribers’ daily commute behaviors by three levels: location trajectory, one-day pattern, and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain two aspects of information: (1) How people’s typical commute patterns are. (2) How much their commute behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learn model parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human commute behaviors and yields higher accuracy results in trajectory prediction task.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {56},
numpages = {24},
keywords = {mobile phone data, spatio-temporal pattern, Routine mining}
}

@article{10.1145/3201406,
author = {Amornbunchornvej, Chainarong and Brugere, Ivan and Strandburg-Peshkin, Ariana and Farine, Damien R. and Crofoot, Margaret C. and Berger-Wolf, Tanya Y.},
title = {Coordination Event Detection and Initiator Identification in Time Series Data},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {5},
issn = {1556-4681},
url = {https://doi.org/10.1145/3201406},
doi = {10.1145/3201406},
abstract = {Behavior initiation is a form of leadership and is an important aspect of social organization that affects the processes of group formation, dynamics, and decision-making in human societies and other social animal species. In this work, we formalize the Coordination Initiator Inference Problem and propose a simple yet powerful framework for extracting periods of coordinated activity and determining individuals who initiated this coordination, based solely on the activity of individuals within a group during those periods. The proposed approach, given arbitrary individual time series, automatically (1) identifies times of coordinated group activity, (2) determines the identities of initiators of those activities, and (3) classifies the likely mechanism by which the group coordination occurred, all of which are novel computational tasks. We demonstrate our framework on both simulated and real-world data: trajectories tracking of animals as well as stock market data. Our method is competitive with existing global leadership inference methods but provides the first approaches for local leadership and coordination mechanism classification. Our results are consistent with ground-truthed biological data and the framework finds many known events in financial data which are not otherwise reflected in the aggregate NASDAQ index. Our method is easily generalizable to any coordinated time series data from interacting entities.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {53},
numpages = {33},
keywords = {influence, coordination, time series, Leadership}
}

@article{10.1145/3186282,
author = {Li, Bo and Vorobeychik, Yevgeniy},
title = {Evasion-Robust Classification on Binary Domains},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3186282},
doi = {10.1145/3186282},
abstract = {The success of classification learning has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static, but make a deliberate effort to evade the classifiers. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. We first present a general approach based on mixed-integer linear programming (MILP) with constraint generation. This approach is the first to compute an optimal solution to adversarial loss minimization for two general classes of adversarial evasion models in the context of binary feature spaces. To further improve scalability and significantly generalize the scope of the MILP-based method, we propose a principled iterative retraining framework, which can be used with arbitrary classifiers and essentially arbitrary attack models. We show that the retraining approach, when it converges, minimizes an upper bound on adversarial loss. Extensive experiments demonstrate that the mixed-integer programming approach significantly outperforms several state-of-the-art adversarial learning alternatives. Moreover, the retraining framework performs nearly as well, but scales significantly better. Finally, we show that our approach is robust to misspecifications of the adversarial model.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {50},
numpages = {32},
keywords = {adversarial examples, classifier evasion, mixed-integer linear programming, robust learning, Adversarial classification}
}

@article{10.1145/3193573,
author = {Mohammadi, Majid and Atashin, Amir Ahooye and Hofman, Wout and Tan, Yaohua},
title = {Comparison of Ontology Alignment Systems Across Single Matching Task Via the McNemar’s Test},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3193573},
doi = {10.1145/3193573},
abstract = {Ontology alignment is widely used to find the correspondences between different ontologies in diverse fields. After discovering the alignments, several performance scores are available to evaluate them. The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies. The current trend in the alignment evaluation is to put forward a new score (e.g., precision, weighted precision, semantic precision, etc.) and to compare various alignments by juxtaposing the obtained scores. However, it is substantially provocative to select one measure among others for comparison. On top of that, claiming if one system has a better performance than one another cannot be substantiated solely by comparing two scalars. In this article, we propose the statistical procedures that enable us to theoretically favor one system over one another. The McNemar’s test is the statistical means by which the comparison of two ontology alignment systems over one matching task is drawn. The test applies to a 2 \texttimes{} 2 contingency table, which can be constructed in two different ways based on the alignments, each of which has their own merits/pitfalls. The ways of the contingency table construction and various apposite statistics from the McNemar’s test are elaborated in minute detail. In the case of having more than two alignment systems for comparison, the family wise error rate is expected to happen. Thus, the ways of preventing such an error are also discussed. A directed graph visualizes the outcome of the McNemar’s test in the presence of multiple alignment systems. From this graph, it is readily understood if one system is better than one another or if their differences are imperceptible. The proposed statistical methodologies are applied to the systems participated in the OAEI 2016 anatomy track, and also compares several well-known similarity metrics for the same matching problem.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {51},
numpages = {18},
keywords = {McNemar’s test, family-wise error rate, Ontology alignment, OAEI, anatomy}
}

@article{10.1145/3186566,
author = {Nguyen, Minh-Tien and Tran, Duc-Vu and Nguyen, Le-Minh and Phan, Xuan-Hieu},
title = {Exploiting User Posts for Web Document Summarization},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3186566},
doi = {10.1145/3186566},
abstract = {Relevant user posts such as comments or tweets of a Web document provide additional valuable information to enrich the content of this document. When creating user posts, readers tend to borrow salient words or phrases in sentences. This can be considered as word variation. This article proposes a framework that models the word variation aspect to enhance the quality of Web document summarization. Technically, the framework consists of two steps: scoring and selection. In the first step, the social information of a Web document such as user posts is exploited to model intra-relations and inter-relations in lexical and semantic levels. These relations are denoted by a mutual reinforcement similarity graph used to score each sentence and user post. After scoring, summaries are extracted by using a ranking approach or concept-based method formulated in the form of Integer Linear Programming. To confirm the efficiency of our framework, sentence and story highlight extraction tasks were taken as a case study on three datasets in two languages, English and Vietnamese. Experimental results show that: (i) the framework can improve ROUGE-scores compared to state-of-the-art baselines of social context summarization and (ii) the combination of the two relations benefits the sentence extraction of single Web documents.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jun,
articleno = {49},
numpages = {28},
keywords = {information retrieval, summarization, ILP, social context summarization, Data mining, ranking}
}

@article{10.1145/3182181,
author = {Saha, Sriparna and Mitra, Sayantan and Kramer, Stefan},
title = {Exploring Multiobjective Optimization for Multiview Clustering},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3182181},
doi = {10.1145/3182181},
abstract = {We present a new multiview clustering approach based on multiobjective optimization. In contrast to existing clustering algorithms based on multiobjective optimization, it is generally applicable to data represented by two or more views and does not require specifying the number of clusters a priori. The approach builds upon the search capability of a multiobjective simulated annealing based technique, AMOSA, as the underlying optimization technique. In the first version of the proposed approach, an internal cluster validity index is used to assess the quality of different partitionings obtained using different views. A new way of checking the compatibility of these different partitionings is also proposed and this is used as another objective function. A new encoding strategy and some new mutation operators are introduced. Finally, a new way of computing a consensus partitioning from multiple individual partitions obtained on multiple views is proposed. As a baseline and for comparison, two multiobjective based ensemble clustering techniques are proposed to combine the outputs of different simple clustering approaches. The efficacy of the proposed clustering methods is shown for partitioning several real-world datasets having multiple views. To show the practical usefulness of the method, we present results on web-search result clustering, where the task is to find a suitable partitioning of web snippets.},
journal = {ACM Trans. Knowl. Discov. Data},
month = may,
articleno = {44},
numpages = {30},
keywords = {multiobjective optimization, simulated annealing, Multiview classification, search result clustering}
}

@article{10.1145/3182384,
author = {Liu, Hongfu and Fu, Yun},
title = {Consensus Guided Multi-View Clustering},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3182384},
doi = {10.1145/3182384},
abstract = {In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi-view data provide more rich information than traditional single-view data. Fusing heterogeneous information for certain tasks is a core part of multi-view learning, especially for multi-view clustering. Although numerous multi-view clustering algorithms have been proposed, most scholars focus on finding the common space of different views, but unfortunately ignore the benefits from partition level by ensemble clustering. For ensemble clustering, however, there is no interaction between individual partitions from each view and the final consensus one. To fill the gap, we propose a Consensus Guided Multi-View Clustering (CMVC) framework, which incorporates the generation of basic partitions from each view and fusion of consensus clustering in an interactive way, i.e., the consensus clustering guides the generation of basic partitions, and high quality basic partitions positively contribute to the consensus clustering as well. We design a non-trivial optimization solution to formulate CMVC into two iterative k-means clusterings by an approximate calculation. In addition, the generalization of CMVC provides a rich feasibility for different scenarios, and the extension of CMVC with incomplete multi-view clustering further validates the effectiveness for real-world applications. Extensive experiments demonstrate the advantages of CMVC over other widely used multi-view clustering methods in terms of cluster validity, and the robustness of CMVC to some important parameters and incomplete multi-view data.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {42},
numpages = {21},
keywords = {ensemble clustering, utility function, Multi-view clustering}
}

@article{10.1145/3184454,
author = {Chen, Hung-Hsuan},
title = {Behavior2Vec: Generating Distributed Representations of Users’ Behaviors on Products for Recommender Systems},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3184454},
doi = {10.1145/3184454},
abstract = {Most studies on recommender systems target at increasing the click through rate, and hope that the number of orders will increase as well. We argue that clicking and purchasing an item are different behaviors. Thus, we should probably apply different strategies for different objectives, e.g., increase the click through rate, or increase the order rate. In this article, we propose to generate the distributed representations of users’ viewing and purchasing behaviors on an e-commerce website. By leveraging on the cosine distance between the distributed representations of the behaviors on items under different contexts, we can predict a user’s next clicking or purchasing item more precisely, compared to several baseline methods. Perhaps more importantly, we found that the distributed representations may help discover interesting analogies among the products. We may utilize such analogies to explain how two products are related, and eventually apply different recommendation strategies under different scenarios. We developed the Behavior2Vec library for demonstration. The library can be accessed at https://github.com/ncu-dart/behavior2vec/.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {43},
numpages = {20},
keywords = {collaborative filtering, behavior embedding, Distributed representation, learning representations, Word2Vec, matrix factorization}
}

@article{10.1145/3182383,
author = {Wu, Hao and Ning, Yue and Chakraborty, Prithwish and Vreeken, Jilles and Tatti, Nikolaj and Ramakrishnan, Naren},
title = {Generating Realistic Synthetic Population Datasets},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3182383},
doi = {10.1145/3182383},
abstract = {Modern studies of societal phenomena rely on the availability of large datasets capturing attributes and activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are necessary to study disease propagation and intervention measures before implementation. In social science, synthetic population datasets are needed to understand how policy decisions might affect preferences and behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic and procedural characteristics of patient records without violating confidentialities of individuals. To generate such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle to formalize a generative model such that in a statistically well-founded way we can optimally utilize given prior information about the data, and are unbiased otherwise. An efficient inference algorithm is designed to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating underlying data distributions. We evaluate this approach against both simulated data and US census datasets, and demonstrate its feasibility using an epidemic simulation application.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {45},
numpages = {22},
keywords = {Multivariate categorical data, maximum entropy models, synthetic population, probabilistic modeling}
}

@article{10.1145/3186586,
author = {Bressan, Marco and Chierichetti, Flavio and Kumar, Ravi and Leucci, Stefano and Panconesi, Alessandro},
title = {Motif Counting Beyond Five Nodes},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3186586},
doi = {10.1145/3186586},
abstract = {Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural algorithms based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that such algorithms are outperformed by color coding (CC) [2], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC; furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC’s memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that CC can push the limits of the state-of-the-art, both in terms of the size of the input graph and of that of the graphlets.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {48},
numpages = {25},
keywords = {Motif counting, subgraph counting, graph mining, color coding}
}

@article{10.1145/3182392,
author = {Chen, Xiaowei and Lui, John C. S.},
title = {Mining Graphlet Counts in Online Social Networks},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3182392},
doi = {10.1145/3182392},
abstract = {Counting subgraphs is a fundamental analysis task for online social networks (OSNs). Given the sheer size and restricted access of OSN, efficient computation of subgraph counts is highly challenging. Although a number of algorithms have been proposed to estimate the relative counts of subgraphs in OSNs with restricted access, there are only few works which try to solve a more general problem, i.e., counting subgraph frequencies. In this article, we propose an efficient random walk-based framework to estimate the subgraph counts. Our framework generates samples by leveraging consecutive steps of the random walk as well as by observing neighbors of visited nodes. Using the importance sampling technique, we derive unbiased estimators of the subgraph counts. To make better use of the degree information of visited nodes, we also design improved estimators, which increases the accuracy of the estimation with no additional cost. We conduct extensive experimental evaluation on real-world OSNs to confirm our theoretical claims. The experiment results show that our estimators are unbiased, accurate, efficient, and better than the state-of-the-art algorithms. For the Weibo graph with more than 58 million nodes, our method produces estimate of triangle count with an error less than 5% using only 20,000 sampled nodes. Detailed comparison with the state-of-the-art methods demonstrates that our algorithm is 2--10 times more accurate.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {41},
numpages = {38},
keywords = {Graphlet count, random walk, online social networks, Markov chain Monte Carlo}
}

@article{10.1145/3186268,
author = {Nesa, Nashreen and Ghosh, Tania and Banerjee, Indrajit},
title = {IGRM: Improved Grey Relational Model and Its Ensembles for Occupancy Sensing in Internet of Things Applications},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3186268},
doi = {10.1145/3186268},
abstract = {Occupancy detection is one of the many applications of Building Automation Systems (BAS) or Heating, Ventilation, and Air Conditioning (HVAC) control systems, especially, with the rising demand of Internet of Things (IoT) services. This article describes the fusion of data collected from sensors by exploiting their potential to sense occupancy in a room. For this purpose, a sensor test bed is deployed that includes four sensors measuring temperature, relative humidity, distance from the first obstacle, and light along with a Arduino micro-controller to validate our model. In addition, this article proposes three algorithms for efficient fusion of the sensor data that is inspired by the Grey theory. An improved Grey Relational Model (iGRM) is proposed, which acts as the base classifier for the other two algorithms, namely, Grey Relational Model with Bagging (iGRM-BG) and Grey Relational Model with Boosting (iGRM-BT). Furthermore, all three algorithms use a sliding window concept, where only the samples inside the window participate in model training. Also, we have considered varying number of window size for optimal comparison. The algorithms were tested against the experimental data collected through a test bed as well as on a publicly available large dataset, where both the ensemble models, iGRM-BG and iGRM-BT, are seen to enhance the performance of iGRM. The results reveal exceptionally high performances with accuracies above 95% (iGRM) and up to 100% (iGRM-BT) for the experimental dataset and above 98.24% (iGRM) and up to 99.49% (iGRM-BG) using the publicly available dataset. Among the three proposed models, iGRM-BG was observed to outperform both iGRM and iGRM-BT owing to its advantage of being an ensemble model and its robustness against over-fitting.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {47},
numpages = {23},
keywords = {Bagging, boosting, window}
}

@article{10.1145/3183346,
author = {Toth, Edward and Chawla, Sanjay},
title = {GTΔ: Detecting Temporal Changes in Group Stochastic Processes},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3183346},
doi = {10.1145/3183346},
abstract = {Given a portfolio of stocks or a series of frames in a video how do we detect significant changes in a group of values for real-time applications? In this article, we formalize the problem of sequentially detecting temporal changes in a group of stochastic processes. As a solution to this particular problem, we propose the group temporal change (GTΔ) algorithm, a simple yet effective technique for the sequential detection of significant changes in a variety of statistical properties of a group over time. Due to the flexible framework of the GTΔ algorithm, a domain expert is able to select one or more statistical properties that they are interested in monitoring. The usefulness of our proposed algorithm is also demonstrated against state-of-the-art techniques on synthetically generated data as well as on two real-world applications; a portfolio of healthcare stocks over a 20 year period and a video monitoring the activity of our Sun.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {39},
numpages = {24},
keywords = {time series analysis, anomaly detection, Group change detection}
}

@article{10.1145/3178048,
author = {Xie, Wei and Zhu, Feida and Xiao, Jing and Wang, Jianzong},
title = {Social Network Monitoring for Bursty Cascade Detection},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3178048},
doi = {10.1145/3178048},
abstract = {Social network services have become important and efficient platforms for users to share all kinds of information. The capability to monitor user-generated information and detect bursts from information diffusions in these social networks brings value to a wide range of real-life applications, such as viral marketing. However, in reality, as a third party, there is always a cost for gathering information from each user or so-called social network sensor. The question then arises how to select a budgeted set of social network sensors to form the data stream for burst detection without compromising the detection performance. In this article, we present a general sensor selection solution for different burst detection approaches. We formulate this problem as a constraint satisfaction problem that has high computational complexity. To reduce the computational cost, we first reduce most of the constraints by making use of the fact that bursty cascades are rare among the whole population. We then transform the problem into an Linear Programming (LP) problem. Furthermore, we use the sub-gradient method instead of the standard simplex method or interior-point method to solve the LP problem, which makes it possible for our solution to scale up to large social networks. Evaluating our solution on millions of real information cascades, we demonstrate both the effectiveness and efficiency of our approach.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {40},
numpages = {24},
keywords = {sub-gradient method, linear programming, Social network sensors}
}

@article{10.1145/3185059,
author = {Fond, Timothy La and Neville, Jennifer and Gallagher, Brian},
title = {Designing Size Consistent Statistics for Accurate Anomaly Detection in Dynamic Networks},
year = {2018},
issue_date = {July 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {4},
issn = {1556-4681},
url = {https://doi.org/10.1145/3185059},
doi = {10.1145/3185059},
abstract = {An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this article, we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics that avoid confounding due to the most common factors, edge count and node count.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {46},
numpages = {49},
keywords = {Graphs, data mining, statistics}
}

@article{10.1145/3173044,
author = {Peng, Min and Zhu, Jiahui and Wang, Hua and Li, Xuhui and Zhang, Yanchun and Zhang, Xiuzhen and Tian, Gang},
title = {Mining Event-Oriented Topics in Microblog Stream with Unsupervised Multi-View Hierarchical Embedding},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3173044},
doi = {10.1145/3173044},
abstract = {This article presents an unsupervised multi-view hierarchical embedding (UMHE) framework to sufficiently reveal the intrinsic topical knowledge in social events. Event-oriented topics are highly related to such events as it can provide explicit descriptions of what have happened in social community. In many real-world cases, however, it is difficult to include all attributes of microblogs, more often, textual aspects only are available. Traditional topic modelling methods have failed to generate event-oriented topics with the textual aspects, since the inherent relations between topics are often overlooked in these methods. Meanwhile, the metrics in original word vocabulary space might not effectively capture semantic distances. Our UMHE framework overcomes the severe information deficiency and poor feature representation. The UMHE first develops a multi-view Bayesian rose tree to preliminarily generate prior knowledge for latent topics and their relations. With such prior knowledge, we design an unsupervised translation-based hierarchical embedding method to make a better representation of these latent topics. By applying self-adaptive spectral clustering on the embedding space and the original space concomitantly, we eventually extract event-oriented topics in word distributions to express social events. Our framework is purely data-driven and unsupervised, without any external knowledge. Experimental results on TREC Tweets2011 dataset and Sina Weibo dataset demonstrate that the UMHE framework can construct hierarchical structure with high fitness, but also yield topic embeddings with salient semantics; therefore, it can derive event-oriented topics with meaningful descriptions.},
journal = {ACM Trans. Knowl. Discov. Data},
month = apr,
articleno = {38},
numpages = {26},
keywords = {Event-oriented topic, Bayesian rose tree, unsupervised learning, multi-view hierarchical embedding}
}

@article{10.1145/3161885,
author = {Moreno, Sebastian and Neville, Jennifer and Kirshner, Sergey},
title = {Tied Kronecker Product Graph Models to Capture Variance in Network Populations},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3161885},
doi = {10.1145/3161885},
abstract = {Much of the past work on mining and modeling networks has focused on understanding the observed properties of single example graphs. However, in many real-life applications it is important to characterize the structure of populations of graphs. In this work, we analyze the distributional properties of probabilistic generative graph models (PGGMs) for network populations. PGGMs are statistical methods that model the network distribution and match common characteristics of real-world networks. Specifically, we show that most PGGMs cannot reflect the natural variability in graph properties observed across multiple networks because their edge generation process assumes independence among edges. Then, we propose the mixed Kronecker Product Graph Model (mKPGM), a scalable generalization of KPGMs that uses tied parameters to increase the variability of the sampled networks, while preserving the edge probabilities in expectation. We compare mKPGM to several other graph models. The results show that learned mKPGMs accurately represent the characteristics of real-world networks, while also effectively capturing the natural variability in network structure.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {35},
numpages = {40},
keywords = {social network analysis, kronecker product graph models, Graph generation models}
}

@article{10.1145/3168363,
author = {Li, Sheng and Shao, Ming and Fu, Yun},
title = {Multi-View Low-Rank Analysis with Applications to Outlier Detection},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3168363},
doi = {10.1145/3168363},
abstract = {Detecting outliers or anomalies is a fundamental problem in various machine learning and data mining applications. Conventional outlier detection algorithms are mainly designed for single-view data. Nowadays, data can be easily collected from multiple views, and many learning tasks such as clustering and classification have benefited from multi-view data. However, outlier detection from multi-view data is still a very challenging problem, as the data in multiple views usually have more complicated distributions and exhibit inconsistent behaviors. To address this problem, we propose a multi-view low-rank analysis (MLRA) framework for outlier detection in this article. MLRA pursuits outliers from a new perspective, robust data representation. It contains two major components. First, the cross-view low-rank coding is performed to reveal the intrinsic structures of data. In particular, we formulate a regularized rank-minimization problem, which is solved by an efficient optimization algorithm. Second, the outliers are identified through an outlier score estimation procedure. Different from the existing multi-view outlier detection methods, MLRA is able to detect two different types of outliers from multiple views simultaneously. To this end, we design a criterion to estimate the outlier scores by analyzing the obtained representation coefficients. Moreover, we extend MLRA to tackle the multi-view group outlier detection problem. Extensive evaluations on seven UCI datasets, the MovieLens, the USPS-MNIST, and the WebKB datasets demon strate that our approach outperforms several state-of-the-art outlier detection methods.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {32},
numpages = {22},
keywords = {low-rank matrix recovery, Multi-view learning, outlier detection}
}

@article{10.1145/3154410,
author = {Altowim, Yasser and Kalashnikov, Dmitri V. and Mehrotra, Sharad},
title = {ProgressER: Adaptive Progressive Approach to Relational Entity Resolution},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3154410},
doi = {10.1145/3154410},
abstract = {Entity resolution (ER) is the process of identifying which entities in a dataset refer to the same real-world object. In relational ER, the dataset consists of multiple entity-sets and relationships among them. Such relationships cause the resolution of some entities to influence the resolution of other entities. For instance, consider a relational dataset that consists of a set of research paper entities and a set of venue entities. In such a dataset, deciding that two research papers are the same may trigger the fact that their venues are also the same. This article proposes a progressive approach to relational ER, named ProgressER, that aims to produce the highest quality result given a constraint on the resolution budget, specified by the user. Such a progressive approach is useful for many emerging analytical applications that require low latency response (and thus cannot tolerate delays caused by cleaning the entire dataset) and/or in situations where the underlying resources are constrained or costly to use. To maximize the quality of the result, ProgressER follows an adaptive strategy that periodically monitors and reassesses the resolution progress to determine which parts of the dataset should be resolved next and how they should be resolved. More specifically, ProgressER divides the input budget into several resolution windows and analyzes the resolution progress at the beginning of each window to generate a resolution plan for the current window. A resolution plan specifies which blocks of entities and which entity pairs within blocks need to be resolved during the plan execution phase of that window. In addition, ProgressER specifies, for each identified pair of entities, the order in which the similarity functions should be applied on the pair. Such an order plays a significant role in reducing the overall cost because applying the first few functions in this order might be sufficient to resolve the pair. The empirical evaluation of ProgressER demonstrates its significant advantage in terms of progressiveness over the traditional ER techniques for the given problem settings.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {33},
numpages = {45},
keywords = {resolution workflow, resolution plan, relational entity resolution, progressive computation, collective entity resolution, Data cleaning, entity resolution}
}

@article{10.1145/3178112,
author = {Bakerman, Jordan and Pazdernik, Karl and Wilson, Alyson and Fairchild, Geoffrey and Bahran, Rian},
title = {Twitter Geolocation: A Hybrid Approach},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3178112},
doi = {10.1145/3178112},
abstract = {Geotagging Twitter messages is an important tool for event detection and enrichment. Despite the availability of both social media content and user network information, these two features are generally utilized separately in the methodology. In this article, we create a hybrid method that uses Twitter content and network information jointly as model features. We use Gaussian mixture models to map the raw spatial distribution of the model features to a predicted field. This approach is scalable to large datasets and provides a natural representation of model confidence. Our method is tested against other approaches and we achieve greater prediction accuracy. The model also improves both precision and coverage.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {34},
numpages = {17},
keywords = {Geotag, Gaussian mixture model, simple accuracy error, prediction region area, Twitter, comprehensive accuracy error}
}

@article{10.1145/3162050,
author = {Khodadadi, Ali and Hosseini, Seyed Abbas and Tavakoli, Erfan and Rabiee, Hamid R.},
title = {Continuous-Time User Modeling in Presence of Badges: A Probabilistic Approach},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3162050},
doi = {10.1145/3162050},
abstract = {User modeling plays an important role in delivering customized web services to the users and improving their engagement. However, most user models in the literature do not explicitly consider the temporal behavior of users. More recently, continuous-time user modeling has gained considerable attention and many user behavior models have been proposed based on temporal point processes. However, typical point process-based models often considered the impact of peer influence and content on the user participation and neglected other factors. Gamification elements are among those factors that are neglected, while they have a strong impact on user participation in online services. In this article, we propose interdependent multi-dimensional temporal point processes that capture the impact of badges on user participation besides the peer influence and content factors. We extend the proposed processes to model user actions over the community-based question and answering websites, and propose an inference algorithm based on Variational-Expectation Maximization that can efficiently learn the model parameters. Extensive experiments on both synthetic and real data gathered from Stack Overflow show that our inference algorithm learns the parameters efficiently and the proposed method can better predict the user behavior compared to the alternatives.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {37},
numpages = {30},
keywords = {badge, temporal point process, variational EM, User modeling, stack overflow, gamification, user profiling}
}

@article{10.1145/3178113,
author = {Yang, Pei and Tan, Qi and He, Jingrui},
title = {Function-on-Function Regression with Mode-Sparsity Regularization},
year = {2018},
issue_date = {April 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {3},
issn = {1556-4681},
url = {https://doi.org/10.1145/3178113},
doi = {10.1145/3178113},
abstract = {Functional data is ubiquitous in many domains, such as healthcare, social media, manufacturing process, sensor networks, and so on. The goal of function-on-function regression is to build a mapping from functional predictors to functional response. In this article, we propose a novel function-on-function regression model based on mode-sparsity regularization. The main idea is to represent the regression coefficient function between predictor and response as the double expansion of basis functions, and then use a mode-sparsity regularization to automatically filter out irrelevant basis functions for both predictors and responses. The proposed approach is further extended to the tensor version to accommodate multiple functional predictors. While allowing the dimensionality of the regression weight matrix or tensor to be relatively large, the mode-sparsity regularized model facilitates the multi-way shrinking of basis functions for each mode. The proposed mode-sparsity regularization covers a wide spectrum of sparse models for function-on-function regression. The resulting optimization problem is challenging due to the non-smooth property of the mode-sparsity regularization. We develop an efficient algorithm to solve the problem, which works in an iterative update fashion, and converges to the global optimum. Furthermore, we analyze the generalization performance of the proposed method and derive an upper bound for the consistency between the recovered function and the underlying true function. The effectiveness of the proposed approach is verified on benchmark functional datasets in various domains.},
journal = {ACM Trans. Knowl. Discov. Data},
month = mar,
articleno = {36},
numpages = {23},
keywords = {Function-on-function regression, mode-sparsity regularization}
}

@article{10.1145/3181707,
author = {van Leeuwen, Matthijs and Chau, Polo and Vreeken, Jilles and Shahaf, Dafna and Faloutsos, Christos},
title = {Editorial: TKDD Special Issue on Interactive Data Exploration and Analytics},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3181707},
doi = {10.1145/3181707},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {1},
numpages = {1}
}

@article{10.1145/3051127,
author = {Yang, Yang and Tang, Jie and Li, Juanzi},
title = {Learning to Infer Competitive Relationships in Heterogeneous Networks},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3051127},
doi = {10.1145/3051127},
abstract = {Detecting and monitoring competitors is fundamental to a company to stay ahead in the global market. Existing studies mainly focus on mining competitive relationships within a single data source, while competing information is usually distributed in multiple networks. How to discover the underlying patterns and utilize the heterogeneous knowledge to avoid biased aspects in this issue is a challenging problem. In this article, we study the problem of mining competitive relationships by learning across heterogeneous networks. We use Twitter and patent records as our data sources and statistically study the patterns behind the competitive relationships. We find that the two networks exhibit different but complementary patterns of competitions. Overall, we find that similar entities tend to be competitors, with a probability of 4 times higher than chance. On the other hand, in social network, we also find a 10 minutes phenomenon: when two entities are mentioned by the same user within 10 minutes, the likelihood of them being competitors is 25 times higher than chance. Based on the discovered patterns, we propose a novel Topical Factor Graph Model. Generally, our model defines a latent topic layer to bridge the Twitter network and patent network. It then employs a semi-supervised learning algorithm to classify the relationships between entities (e.g., companies or products). We test the proposed model on two real data sets and the experimental results validate the effectiveness of our model, with an average of +46% improvement over alternative methods. Besides, we further demonstrate the competitive relationships inferred by our proposed model can be applied in the job-hopping prediction problem by achieving an average of +10.7% improvement.},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {12},
numpages = {23},
keywords = {heterogeneous network, Social network, competitive relationship}
}

@article{10.1145/3070648,
author = {Kamat, Niranjan and Nandi, Arnab},
title = {A Session-Based Approach to Fast-But-Approximate Interactive Data Cube Exploration},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3070648},
doi = {10.1145/3070648},
abstract = {With the proliferation of large datasets, sampling has become pervasive in data analysis. Sampling has numerous benefits—from reducing the computation time and cost to increasing the scope of interactive analysis. A popular task in data science, well-suited toward sampling, is the computation of fast-but-approximate aggregations over sampled data. Aggregation is a foundational block of data analysis, with data cube being its primary construct. We observe that such aggregation queries are typically issued in an ad-hoc, interactive setting. In contrast to one-off queries, a typical query session consists of a series of quick queries, interspersed with the user inspecting the results and formulating the next query. The similarity between session queries opens up opportunities for reusing computation of not just query results, but also error estimates. Error estimates need to be provided alongside sampled results for the results to be meaningful. We propose Sesame, a rewrite and caching framework that accelerates the entire interactive <underline>ses</underline>sion of aggregation queries over <underline>sam</underline>pl<underline>e</underline>d data. We focus on two unique and computationally expensive aspects of this use case: query speculation in the presence of sampling, and error computation, and provide novel strategies for result and error reuse. We demonstrate that our approach outperforms conventional sampled aggregation techniques by at least an order of magnitude, without modifying the underlying database.},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {9},
numpages = {26},
keywords = {faceted exploration, interactive visualization, Error Reuse, session, aggregation}
}

@article{10.1145/3051126,
author = {Senin, Pavel and Lin, Jessica and Wang, Xing and Oates, Tim and Gandhi, Sunil and Boedihardjo, Arnold P. and Chen, Crystal and Frankenstein, Susan},
title = {GrammarViz 3.0: Interactive Discovery of Variable-Length Time Series Patterns},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3051126},
doi = {10.1145/3051126},
abstract = {The problems of recurrent and anomalous pattern discovery in time series, e.g., motifs and discords, respectively, have received a lot of attention from researchers in the past decade. However, since the pattern search space is usually intractable, most existing detection algorithms require that the patterns have discriminative characteristics and have its length known in advance and provided as input, which is an unreasonable requirement for many real-world problems. In addition, patterns of similar structure, but of different lengths may co-exist in a time series. Addressing these issues, we have developed algorithms for variable-length time series pattern discovery that are based on symbolic discretization and grammar inference—two techniques whose combination enables the structured reduction of the search space and discovery of the candidate patterns in linear time. In this work, we present GrammarViz 3.0—a software package that provides implementations of proposed algorithms and graphical user interface for interactive variable-length time series pattern discovery. The current version of the software provides an alternative grammar inference algorithm that improves the time series motif discovery workflow, and introduces an experimental procedure for automated discretization parameter selection that builds upon the minimum cardinality maximum cover principle and aids the time series recurrent and anomalous pattern discovery.},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {10},
numpages = {28},
keywords = {Interactive data mining}
}

@article{10.1145/3023363,
author = {Shi, Lei and Tong, Hanghang and Daianu, Madelaine and Tian, Feng and Thompson, Paul M.},
title = {Visual Analysis of Brain Networks Using Sparse Regression Models},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3023363},
doi = {10.1145/3023363},
abstract = {Studies of the human brain network are becoming increasingly popular in the fields of neuroscience, computer science, and neurology. Despite this rapidly growing line of research, gaps remain on the intersection of data analytics, interactive visual representation, and the human intelligence—all needed to advance our understanding of human brain networks. This article tackles this challenge by exploring the design space of visual analytics. We propose an integrated framework to orchestrate computational models with comprehensive data visualizations on the human brain network. The framework targets two fundamental tasks: the visual exploration of multi-label brain networks and the visual comparison among brain networks across different subject groups. During the first task, we propose a novel interactive user interface to visualize sets of labeled brain networks; in our second task, we introduce sparse regression models to select discriminative features from the brain network to facilitate the comparison. Through user studies and quantitative experiments, both methods are shown to greatly improve the visual comparison performance. Finally, real-world case studies with domain experts demonstrate the utility and effectiveness of our framework to analyze reconstructions of human brain connectivity maps. The perceptually optimized visualization design and the feature selection model calibration are shown to be the key to our significant findings.},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {5},
numpages = {30},
keywords = {feature selection, visual analysis, connectome, Brain network}
}

@article{10.1145/3047010,
author = {Makki, Raheleh and Carvalho, Eder and Soto, Axel J. and Brooks, Stephen and Oliveira, Maria Cristina Ferreira De and Milios, Evangelos and Minghim, Rosane},
title = {ATR-Vis: Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3047010},
doi = {10.1145/3047010},
abstract = {The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers’ actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This article presents Active Tweet Retrieval Visualization (ATR-Vis), a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter datasets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.},
journal = {ACM Trans. Knowl. Discov. Data},
month = feb,
articleno = {3},
numpages = {33},
keywords = {Information retrieval, active learning, visual analytics}
}

@article{10.1145/3007212,
author = {Galbrun, Esther and Miettinen, Pauli},
title = {Mining Redescriptions with Siren},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3007212},
doi = {10.1145/3007212},
abstract = {In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process toward results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this article, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {6},
numpages = {30},
keywords = {Redescription mining, visual data mining, interactive data mining}
}

@article{10.1145/3047017,
author = {Wu, Hao and Sun, Maoyuan and Mi, Peng and Tatti, Nikolaj and North, Chris and Ramakrishnan, Naren},
title = {Interactive Discovery of Coordinated Relationship Chains with Maximum Entropy Models},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3047017},
doi = {10.1145/3047017},
abstract = {Modern visual analytic tools promote human-in-the-loop analysis but are limited in their ability to direct the user toward interesting and promising directions of study. This problem is especially acute when the analysis task is exploratory in nature, e.g., the discovery of potentially coordinated relationships in massive text datasets. Such tasks are very common in domains like intelligence analysis and security forensics where the goal is to uncover surprising coalitions bridging multiple types of relations. We introduce new maximum entropy models to discover surprising chains of relationships leveraging count data about entity occurrences in documents. These models are embedded in a visual analytic system called MERCER (Maximum Entropy Relational Chain ExploRer) that treats relationship bundles as first class objects and directs the user toward promising lines of inquiry. We demonstrate how user input can judiciously direct analysis toward valid conclusions, whereas a purely algorithmic approach could be led astray. Experimental results on both synthetic and real datasets from the intelligence community are presented.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {7},
numpages = {34},
keywords = {Maximum entropy models, multi-relational pattern mining, interactive visual data exploration}
}

@article{10.1145/3047011,
author = {Rayar, Fr\'{e}d\'{e}ric and Barrat, Sabine and Bouali, Fatma and Venturini, Gilles},
title = {A Viewable Indexing Structure for the Interactive Exploration of Dynamic and Large Image Collections},
year = {2018},
issue_date = {February 2018},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {1556-4681},
url = {https://doi.org/10.1145/3047011},
doi = {10.1145/3047011},
abstract = {Thanks to the capturing devices cost reduction and the advent of social networks, the size of image collections is becoming extremely huge. Many works in the literature have addressed the indexing of large image collections for search purposes. However, there is a lack of support for exploratory data mining. One may want to wander around the images and experience serendipity in the exploration process. Thus, effective paradigms not only for organising, but also visualising these image collections become necessary. In this article, we present a study to jointly index and visualise large image collections. The work focuses on satisfying three constraints. First, large image collections, up to million of images, shall be handled. Second, dynamic collections, such as ever-growing collections, shall be processed in an incremental way, without reprocessing the whole collection at each modification. Finally, an intuitive and interactive exploration system shall be provided to the user to allow him to easily mine image collections. To this end, a data partitioning algorithm has been modified and proximity graphs have been used to fit the visualisation purpose. A custom web platform has been implemented to visualise the hierarchical and graph-based hybrid structure. The results of a user evaluation we have conducted show that the exploration of the collections is intuitive and smooth thanks to the proposed structure. Furthermore, the scalability of the proposed indexing method is proved using large public image collections.},
journal = {ACM Trans. Knowl. Discov. Data},
month = jan,
articleno = {2},
numpages = {26},
keywords = {dynamic collections, large image collections, Interactive visualisation, incremental construction}
}

@article{10.1

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main