text:
Inferring and Embedding Diffusion Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623646},
doi = {10.1145/2623330.2623646},
abstract = {The diffusion of information, rumors, and diseases are assumed to be probabilistic processes over some network structure. An event starts at one node of the network, and then spreads to the edges of the network. In most cases, the underlying network structure that generates the diffusion process is unobserved, and we only observe the times at which each node is altered/influenced by the process. This paper proposes a probabilistic model for inferring the diffusion network, which we call Probabilistic Latent Network Visualization (PLNV); it is based on cascade data, a record of observed times of node influence. An important characteristic of our approach is to infer the network by embedding it into a low-dimensional visualization space. We assume that each node in the network has latent coordinates in the visualization space, and diffusion is more likely to occur between nodes that are placed close together. Our model uses maximum a posteriori estimation to learn the latent coordinates of nodes that best explain the observed cascade data. The latent coordinates of nodes in the visualization space can 1) enable the system to suggest network layouts most suitable for browsing, and 2) lead to high accuracy in inferring the underlying network when analyzing the diffusion process of new or rare information, rumors, and disease.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1236–1245},
numpages = {10},
keywords = {diffusion network, survival analysis, network visualization},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623728,
author = {Wang, Senzhang and Hu, Xia and Yu, Philip S. and Li, Zhoujun},
title = {MMRate: Inferring Multi-Aspect Diffusion Networks with Multi-Pattern Cascades},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623728},
doi = {10.1145/2623330.2623728},
abstract = {Inferring diffusion networks from traces of cascades has been extensively studied to better understand information diffusion in many domains. A widely used assumption in previous work is that the diffusion network is homogenous and diffusion processes of cascades follow the same pattern. However, in social media, users may have various interests and the connections among them are usually multi-faceted. In addition, different cascades normally diffuse at different speeds and spread to diverse scales, and hence show various diffusion patterns. It is challenging for traditional models to capture the heterogeneous user interactions and diverse patterns of cascades in social media. In this paper, we investigate a novel problem of inferring multi-aspect diffusion networks with multi-pattern cascades. In particular, we study the effects of various diffusion patterns on the information diffusion process by analyzing users' retweeting behavior on a microblogging dataset. By incorporating aspect-level user interactions and various diffusion patterns, a new model for inferring Multi-aspect transmission Rates between users using Multi-pattern cascades (MMRate) is proposed. We also provide an Expectation Maximization algorithm to effectively estimate the parameters. Experimental results on both synthetic and microblogging datasets demonstrate the superior performance of our approach over the state-of-the-art methods in inferring multi-aspect diffusion networks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1246–1255},
numpages = {10},
keywords = {graph mining, information diffusion, social network},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623746,
author = {He, Xinran and Kempe, David},
title = {Stability of Influence Maximization},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623746},
doi = {10.1145/2623330.2623746},
abstract = {The present article serves as an erratum to our paper of the same title, which was presented and published in the KDD 2014 conference. In that article, we claimed falsely that the objective function defined in Section 1.4 is non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example to that claim.Subsequent to becoming aware of the counter-example, we have shown that the objective function is in fact NP-hard to approximate to within a factor of O(n1-ε) for any ε > 0.In an attempt to fix the record, the present article combines the problem motivation, models, and experimental results sections from the original incorrect article with the new hardness result. We would like readers to only cite and use this version (which will remain an unpublished note) instead of the incorrect conference version.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1256–1265},
numpages = {10},
keywords = {robust optimization, uncertainty, noise, submodular optimization, influence maximization},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623733,
author = {Barbieri, Nicola and Bonchi, Francesco and Manco, Giuseppe},
title = {Who to Follow and Why: Link Prediction with Explanations},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623733},
doi = {10.1145/2623330.2623733},
abstract = {User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty.In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW ("Who to Follow and Why"), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a "topical" or a "social" link, and depending on this decision it produces a different type of explanation.A topical link is recommended between a user interested in a topic and a user authoritative in that topic: the explanation in this case is a set of binary features describing the topic responsible of the link creation. A social link is recommended between users which share a large social neighborhood: in this case the explanation is the set of neighbors which are more likely to be responsible for the link creation.Our experimental assessment on real-world data confirms the accuracy of WTFW in the link prediction and the quality of the associated explanations.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1266–1275},
numpages = {10},
keywords = {link prediction, social networks},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623737,
author = {Zhou, Yang and Liu, Ling},
title = {Activity-Edge Centric Multi-Label Classification for Mining Heterogeneous Information Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623737},
doi = {10.1145/2623330.2623737},
abstract = {Multi-label classification of heterogeneous information networks has received renewed attention in social network analysis. In this paper, we present an activity-edge centric multi-label classification framework for analyzing heterogeneous information networks with three unique features. First, we model a heterogeneous information network in terms of a collaboration graph and multiple associated activity graphs. We introduce a novel concept of vertex-edge homophily in terms of both vertex labels and edge labels and transform a general collaboration graph into an activity-based collaboration multigraph by augmenting its edges with class labels from each activity graph through activity-based edge classification. Second, we utilize the label vicinity to capture the pairwise vertex closeness based on the labeling on the activity-based collaboration multigraph. We incorporate both the structure affinity and the label vicinity into a unified classifier to speed up the classification convergence. Third, we design an iterative learning algorithm, AEClass, to dynamically refine the classification result by continuously adjusting the weights on different activity-based edge classification schemes from multiple activity graphs, while constantly learning the contribution of the structure affinity and the label vicinity in the unified classifier. Extensive evaluation on real datasets demonstrates that AEClass outperforms existing representative methods in terms of both effectiveness and efficiency.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1276–1285},
numpages = {10},
keywords = {label vicinity, collaboration multigraph, activity-based edge classification, multi-label classification, heterogeneous network},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623645,
author = {Zhang, Jiawei and Yu, Philip S. and Zhou, Zhi-Hua},
title = {Meta-Path Based Multi-Network Collective Link Prediction},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623645},
doi = {10.1145/2623330.2623645},
abstract = {Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 "intra-network social meta paths" and 4 categories of "inter-network social meta paths" are proposed in this paper. These "social meta paths" can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative "social meta paths" are picked, the process of which is formally defined as "social meta path selection" in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected "social meta paths" in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1286–1295},
numpages = {10},
keywords = {transfer learning, data mining, classification, link prediction},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623701,
author = {Purohit, Manish and Prakash, B. Aditya and Kang, Chanhyun and Zhang, Yao and Subrahmanian, V.S.},
title = {Fast Influence-Based Coarsening for Large Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623701},
doi = {10.1145/2623330.2623701},
abstract = {Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications.In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm COARSENET for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of COARSENET, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1296–1305},
numpages = {10},
keywords = {graph mining, propagation, coarsening, diffusion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623684,
author = {Zhang, Peng and Chen, Wei and Sun, Xiaoming and Wang, Yajun and Zhang, Jialin},
title = {Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623684},
doi = {10.1145/2623330.2623684},
abstract = {A topic propagating in a social network reaches its tipping point if the number of users discussing it in the network exceeds a critical threshold such that a wide cascade on the topic is likely to occur. In this paper, we consider the task of selecting initial seed users of a topic with minimum size so that {em with a guaranteed probability} the number of users discussing the topic would reach a given threshold. We formulate the task as an optimization problem called {em seed minimization with probabilistic coverage guarantee (SM-PCG)}. This problem departs from the previous studies on social influence maximization or seed minimization because it considers influence coverage with {em probabilistic} guarantees instead of guarantees on {em expected} influence coverage. We show that the problem is not submodular, and thus is harder than previously studied problems based on submodular function optimization. We provide an approximation algorithm and show that it approximates the optimal solution with both a multiplicative ratio and an additive error. The multiplicative ratio is tight while the additive error would be small if influence coverage distributions of certain seed sets are well concentrated. For one-way bipartite graphs we analytically prove the concentration condition and obtain an approximation algorithm with an $O(log n)$ multiplicative ratio and an $O(sqrt{n})$ additive error, where $n$ is the total number of nodes in the social graph. Moreover, we empirically verify the concentration condition in real-world networks and experimentally demonstrate the effectiveness of our proposed algorithm comparing to commonly adopted benchmark algorithms.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1306–1315},
numpages = {10},
keywords = {seed minimization, social networks, independent cascade model, influence diffusion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623655,
author = {Bonchi, Francesco and Gullo, Francesco and Kaltenbrunner, Andreas and Volkovich, Yana},
title = {Core Decomposition of Uncertain Graphs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623655},
doi = {10.1145/2623330.2623655},
abstract = {Core decomposition has proven to be a useful primitive for a wide range of graph analyses. One of its most appealing features is that, unlike other notions of dense subgraphs, it can be computed linearly in the size of the input graph. In this paper we provide an analogous tool for uncertain graphs, i.e., graphs whose edges are assigned a probability of existence. The fact that core decomposition can be computed efficiently in deterministic graphs does not guarantee efficiency in uncertain graphs, where even the simplest graph operations may become computationally intensive. Here we show that core decomposition of uncertain graphs can be carried out efficiently as well.We extensively evaluate our definitions and methods on a number of real-world datasets and applications, such as influence maximization and task-driven team formation.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1316–1325},
numpages = {10},
keywords = {uncertain graphs, dense subgraph, core decomposition},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623718,
author = {Benson, Austin R. and Riquelme, Carlos and Schmit, Sven},
title = {Learning Multifractal Structure in Large Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623718},
doi = {10.1145/2623330.2623718},
abstract = {Using random graphs to model networks has a rich history. In this paper, we analyze and improve the multifractal network generators (MFNG) introduced by Palla et al. We provide a new result on the probability of subgraphs existing in graphs generated with MFNG. This allows us to quickly compute moments of an important set of graph properties, such as the expected number of edges, stars, and cliques for graphs generated using MFNG. Specifically, we show how to compute these moments in time complexity independent of the size of the graph and the number of recursive levels in the generative model. We leverage this theory to propose a new method of moments algorithm for fitting MFNG to large networks. Empirically, this new approach effectively simulates properties of several social and information networks. In terms of matching subgraph counts, our method outperforms similar algorithms used with the Stochastic Kronecker Graph model. Furthermore, we present a fast approximation algorithm to generate graph instances following the multifractal structure. The approximation scheme is an improvement over previous methods, which ran in time complexity quadratic in the number of vertices. Combined, our method of moments and fast sampling scheme provide the first scalable framework for effectively modeling large networks with MFNG.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1326–1335},
numpages = {10},
keywords = {stochastic kronecker graph, graph sampling, graph mining, method of moments, multifractal, random graphs, real-world networks},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623741,
author = {Liu, Chuanren and Zhang, Kai and Xiong, Hui and Jiang, Geoff and Yang, Qiang},
title = {Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623741},
doi = {10.1145/2623330.2623741},
abstract = {Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, more and more symbols are often needed to encode a meaningful sequence. This is so-called 'curse of cardinality', which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, new visions and strategies are needed to face the challenges. To this end, in this paper, we propose a 'temporal skeletonization' approach to proactively reduce the representation of sequences to uncover significant, hidden temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the 'skeleton' of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. In the meantime, the embedding topology of the graph allows us to translate the rich temporal content into a metric space. This opens up new possibilities to explore, quantify, and visualize sequential data. Our approach has shown to greatly alleviate the curse of cardinality in challenging tasks of sequential pattern mining and clustering. Evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy customer event data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1336–1345},
numpages = {10},
keywords = {curse of cardinality, network embedding, sequential pattern mining, temporal skeletonization},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623682,
author = {Perozzi, Bryan and Akoglu, Leman and Iglesias S\'{a}nchez, Patricia and M\"{u}ller, Emmanuel},
title = {Focused Clustering and Outlier Detection in Large Attributed Graphs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623682},
doi = {10.1145/2623330.2623682},
abstract = {Graph clustering and graph outlier detection have been studied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with attributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer algorithms to more interesting parts of the attributed graph. In this work, we overcome this limitation and introduce a novel user-oriented approach for mining attributed graphs. The key aspect of our approach is to infer user preference by the so-called focus attributes through a set of user-provided exemplar nodes. In this new problem setting, clusters and outliers are then simultaneously mined according to this user preference. Specifically, our FocusCO algorithm identifies the focus, extracts focused clusters and detects outliers. Moreover, FocusCO scales well with graph size, since we perform a local clustering of interest to the user rather than global partitioning of the entire graph. We show the effectiveness and scalability of our method on synthetic and real-world graphs, as compared to both existing graph clustering and outlier detection approaches.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1346–1355},
numpages = {10},
keywords = {focused graph mining, distance metric learning, outlier mining, clustering, attributed graphs, infer user preference},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623643,
author = {Ni, Jingchao and Tong, Hanghang and Fan, Wei and Zhang, Xiang},
title = {Inside the Atoms: Ranking on a Network of Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623643},
doi = {10.1145/2623330.2623643},
abstract = {Networks are prevalent and have posed many fascinating research questions. How can we spot similar users, e.g., virtual identical twins, in Cleveland for a New Yorker? Given a query disease, how can we prioritize its candidate genes by incorporating the tissue-specific protein interaction networks of those similar diseases? In most, if not all, of the existing network ranking methods, the nodes are the ranking objects with the finest granularity. In this paper, we propose a new network data model, a Network of Networks (NoN), where each node of the main network itself can be further represented as another (domain-specific) network. This new data model enables to compare the nodes in a broader context and rank them at a finer granularity. Moreover, such an NoN model enables much more efficient search when the ranking targets reside in a certain domain-specific network. We formulate ranking on NoN as a regularized optimization problem; propose efficient algorithms and provide theoretical analysis, such as optimality, convergence, complexity and equivalence. Extensive experimental evaluations demonstrate the effectiveness and the efficiency of our methods.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1356–1365},
numpages = {10},
keywords = {ranking, network of networks, query},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623621,
author = {Kloumann, Isabel M. and Kleinberg, Jon M.},
title = {Community Membership Identification from Small Seed Sets},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623621},
doi = {10.1145/2623330.2623621},
abstract = {In many applications we have a social network of people and would like to identify the members of an interesting but unlabeled group or community. We start with a small number of exemplar group members -- they may be followers of a political ideology or fans of a music genre -- and need to use those examples to discover the additional members. This problem gives rise to the seed expansion problem in community detection: given example community members, how can the social graph be used to predict the identities of remaining, hidden community members? In contrast with global community detection (graph partitioning or covering), seed expansion is best suited for identifying communities locally concentrated around nodes of interest. A growing body of work has used seed expansion as a scalable means of detecting overlapping communities. Yet despite growing interest in seed expansion, there are divergent approaches in the literature and there still isn't a systematic understanding of which approaches work best in different domains. Here we evaluate several variants and uncover subtle trade-offs between different approaches. We explore which properties of the seed set can improve performance, focusing on heuristics that one can control in practice. As a consequence of this systematic understanding we have found several opportunities for performance gains. We also consider an adaptive version in which requests are made for additional membership labels of particular nodes, such as one finds in field studies of social communities. This leads to interesting connections and contrasts with active learning and the trade-offs of exploration and exploitation. Finally, we explore topological properties of communities and seed sets that correlate with algorithm performance, and explain these empirical observations with theoretical ones. We evaluate our methods across multiple domains, using publicly available datasets with labeled, ground-truth communities.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1366–1375},
numpages = {10},
keywords = {ground-truth communities, seed set expansion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623629,
author = {Duan, Lian and Street, Willian Nick and Liu, Yanchi and Lu, Haibing},
title = {Community Detection in Graphs through Correlation},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623629},
doi = {10.1145/2623330.2623629},
abstract = {Community detection is an important task for social networks, which helps us understand the functional modules on the whole network. Among different community detection methods based on graph structures, modularity-based methods are very popular recently, but suffer a well-known resolution limit problem. This paper connects modularity-based methods with correlation analysis by subtly reformatting their math formulas and investigates how to fully make use of correlation analysis to change the objective function of modularity-based methods, which provides a more natural and effective way to solve the resolution limit problem. In addition, a novel theoretical analysis on the upper bound of different objective functions helps us understand their bias to different community sizes, and experiments are conducted on both real life and simulated data to validate our findings.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1376–1385},
numpages = {10},
keywords = {modularity, community detection, leverage, likelihood ratio, correlation analysis},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623706,
author = {Kloster, Kyle and Gleich, David F.},
title = {Heat Kernel Based Community Detection},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623706},
doi = {10.1145/2623330.2623706},
abstract = {The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank diffusion, is useful in identifying a community nearby a starting seed node. We present the first deterministic, local algorithm to compute this diffusion and use that algorithm to study the communities that it produces. Our algorithm is formally a relaxation method for solving a linear system to estimate the matrix exponential in a degree-weighted norm. We prove that this algorithm stays localized in a large graph and has a worst-case constant runtime that depends only on the parameters of the diffusion, not the size of the graph. On large graphs, our experiments indicate that the communities produced by this method have better conductance than those produced by PageRank, although they take slightly longer to compute. On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1386–1395},
numpages = {10},
keywords = {heat kernel, local clustering},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623707,
author = {Chakraborty, Tanmoy and Srinivasan, Sriram and Ganguly, Niloy and Mukherjee, Animesh and Bhowmick, Sanjukta},
title = {On the Permanence of Vertices in Network Communities},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623707},
doi = {10.1145/2623330.2623707},
abstract = {Despite the prevalence of community detection algorithms, relatively less work has been done on understanding whether a network is indeed modular and how resilient the community structure is under perturbations. To address this issue, we propose a new vertex-based metric called "permanence", that can quantitatively give an estimate of the community- like structure of the network.The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon the following two factors: (i) the distribution of external connectivity of the vertex to individual communities and not the total external connectivity, and (ii) the strength of its internal connectivity and not just the total internal edges.In this paper, we demonstrate that compared to other metrics, permanence provides (i) a more accurate estimate of a derived community structure to the ground-truth community and (ii) is more sensitive to perturbations in the network. As a by-product of this study, we have also developed a community detection algorithm based on maximizing permanence. For a modular network structure, the results of our algorithm match well with ground-truth communities.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1396–1405},
numpages = {10},
keywords = {permanence, community analysis, modularity},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623738,
author = {Ghosh, Rumi and Teng, Shang-hua and Lerman, Kristina and Yan, Xiaoran},
title = {The Interplay between Dynamics and Networks: Centrality, Communities, and Cheeger Inequality},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623738},
doi = {10.1145/2623330.2623738},
abstract = {We study the interplay between a dynamic process and the structure of the network on which it is defined. Specifically, we examine the impact of this interaction on the quality-measure of network clusters and node centrality. This enables us to effectively identify network communities and important nodes participating in the dynamics. As the first step towards this objective, we introduce an umbrella framework for defining and characterizing an ensemble of dynamic processes on a network. This framework generalizes the traditional Laplacian framework to continuous-time biased random walks and also allows us to model some epidemic processes over a network. For each dynamic process in our framework, we can define a function that measures the quality of every subset of nodes as a potential cluster (or community) with respect to this process on a given network. This subset-quality function generalizes the traditional conductance measure for graph partitioning. We partially justify our choice of the quality function by showing that the classic Cheeger's inequality, which relates the conductance of the best cluster in a network with a spectral quantity of its Laplacian matrix, can be extended from the Laplacian-conductance setting to this more general setting.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1406–1415},
numpages = {10},
keywords = {community detection, graph theory, spectral clustering, Cheeger's inequality, centrality},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623626,
author = {Yoshida, Yuichi},
title = {Almost Linear-Time Algorithms for Adaptive Betweenness Centrality Using Hypergraph Sketches},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623626},
doi = {10.1145/2623330.2623626},
abstract = {Betweenness centrality measures the importance of a vertex by quantifying the number of times it acts as a midpoint of the shortest paths between other vertices. This measure is widely used in network analysis. In many applications, we wish to choose the k vertices with the maximum adaptive betweenness centrality, which is the betweenness centrality without considering the shortest paths that have been taken into account by already-chosen vertices. All previous methods are designed to compute the betweenness centrality in a fixed graph. Thus, to solve the above task, we have to run these methods $k$ times. In this paper, we present a method that directly solves the task, with an almost linear runtime no matter how large the value of k. Our method first constructs a hypergraph that encodes the betweenness centrality, and then computes the adaptive betweenness centrality by examining this graph. Our technique can be utilized to handle other centrality measures. We theoretically prove that our method is very accurate, and experimentally confirm that it is three orders of magnitude faster than previous methods. Relying on the scalability of our method, we experimentally demonstrate that strategies based on adaptive betweenness centrality are effective in important applications studied in the network science and database communities.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1416–1425},
numpages = {10},
keywords = {adaptive betweenness centrality, randomized algorithm, adaptive coverage centrality},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623745,
author = {Lofgren, Peter A. and Banerjee, Siddhartha and Goel, Ashish and Seshadhri, C.},
title = {FAST-PPR: Scaling Personalized Pagerank Estimation for Large Graphs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623745},
doi = {10.1145/2623330.2623745},
abstract = {We propose a new algorithm, FAST-PPR, for computing personalized PageRank: given start node s and target node t in a directed graph, and given a threshold δ, it computes the Personalized PageRank π_s(t) from s to t, guaranteeing that the relative error is small as long πs(t) > δ. Existing algorithms for this problem have a running-time of Ω(1/δ in comparison, FAST-PPR has a provable average running-time guarantee of O(√d/δ) (where d is the average in-degree of the graph). This is a significant improvement, since δ is often O(1/n) (where n is the number of nodes) for applications. We also complement the algorithm with an Ω(1/√δ) lower bound for PageRank estimation, showing that the dependence on δ cannot be improved.We perform a detailed empirical study on numerous massive graphs, showing that FAST-PPR dramatically outperforms existing algorithms. For example, on the 2010 Twitter graph with 1.5 billion edges, for target nodes sampled by popularity, FAST-PPR has a 20 factor speedup over the state of the art. Furthermore, an enhanced version of FAST-PPR has a 160 factor speedup on the Twitter graph, and is at least 20 times faster on all our candidate graphs.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1436–1445},
numpages = {10},
keywords = {social search, personalized pagerank},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623757,
author = {Ahmed, Nesreen K. and Duffield, Nick and Neville, Jennifer and Kompella, Ramana},
title = {Graph Sample and Hold: A Framework for Big-Graph Analytics},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623757},
doi = {10.1145/2623330.2623757},
abstract = {Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is assumed to mirror every property of the whole population. Unfortunately, such a perfect sample is hard to collect in complex populations such as graphs (e.g. web graphs, social networks), where an underlying network connects the units of the population. Therefore, a good sample will be representative in the sense that graph properties of interest can be estimated with a known degree of accuracy.While previous work focused particularly on sampling schemes to estimate certain graph properties (e.g. triangle count), much less is known for the case when we need to estimate various graph properties with the same sampling scheme. In this paper, we pro- pose a generic stream sampling framework for big-graph analytics, called Graph Sample and Hold (gSH), which samples from massive graphs sequentially in a single pass, one edge at a time, while maintaining a small state in memory. We use a Horvitz-Thompson construction in conjunction with a scheme that samples arriving edges without adjacencies to previously sampled edges with probability p and holds edges with adjacencies with probability q. Our sample and hold framework facilitates the accurate estimation of subgraph patterns by enabling the dependence of the sampling process to vary based on previous history. Within our framework, we show how to produce statistically unbiased estimators for various graph properties from the sample. Given that the graph analytics will run on a sample instead of the whole population, the runtime complexity is kept under control. Moreover, given that the estimators are unbiased, the approximation error is also kept under control. Finally, we test the performance of the proposed framework (gSH) on various types of graphs, showing that from a sample with -- 40K edges, it produces estimates with relative errors < 1%.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1446–1455},
numpages = {10},
keywords = {network sampling, graph streams, statistical estimation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623660,
author = {Bourse, Florian and Lelarge, Marc and Vojnovic, Milan},
title = {Balanced Graph Edge Partition},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623660},
doi = {10.1145/2623330.2623660},
abstract = {Balanced edge partition has emerged as a new approach to partition an input graph data for the purpose of scaling out parallel computations, which is of interest for several modern data analytics computation platforms, including platforms for iterative computations, machine learning problems, and graph databases. This new approach stands in a stark contrast to the traditional approach of balanced vertex partition, where for given number of partitions, the problem is to minimize the number of edges cut subject to balancing the vertex cardinality of partitions. In this paper, we first characterize the expected costs of vertex and edge partitions with and without aggregation of messages, for the commonly deployed policy of placing a vertex or an edge uniformly at random to one of the partitions. We then obtain the first approximation algorithms for the balanced edge-partition problem which for the case of no aggregation matches the best known approximation ratio for the balanced vertex-partition problem, and show that this remains to hold for the case with aggregation up to factor that is equal to the maximum in-degree of a vertex. We report results of an extensive empirical evaluation on a set of real-world graphs, which quantifies the benefits of edge- vs. vertex-partition, and demonstrates efficiency of natural greedy online assignments for the balanced edge-partition problem with and with no aggregation.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1456–1465},
numpages = {10},
keywords = {streaming heuristics, approximation algorithms, distributed massive computation, graph edge partition},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623664,
author = {Sintos, Stavros and Tsaparas, Panayiotis},
title = {Using Strong Triadic Closure to Characterize Ties in Social Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623664},
doi = {10.1145/2623330.2623664},
abstract = {In the past few years there has been an explosion of social networks in the online world. Users flock these networks, creating profiles and linking themselves to other individuals. Connecting online has a small cost compared to the physical world, leading to a proliferation of connections, many of which carry little value or importance. Understanding the strength and nature of these relationships is paramount to anyone interesting in making use of the online social network data. In this paper, we use the principle of Strong Triadic Closure to characterize the strength of relationships in social networks. The Strong Triadic Closure principle stipulates that it is not possible for two individuals to have a strong relationship with a common friend and not know each other. We consider the problem of labeling the ties of a social network as strong or weak so as to enforce the Strong Triadic Closure property. We formulate the problem as a novel combinatorial optimization problem, and we study it theoretically. Although the problem is NP-hard, we are able to identify cases where there exist efficient algorithms with provable approximation guarantees. We perform experiments on real data, and we show that there is a correlation between the labeling we obtain and empirical metrics of tie strength, and that weak edges act as bridges between different communities in the network. Finally, we study extensions and variations of our problem both theoretically and experimentally.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1466–1475},
numpages = {10},
keywords = {strong triadic closure, social networks, approximation algorithms},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623722,
author = {Sun, Huan and Srivatsa, Mudhakar and Tan, Shulong and Li, Yang and Kaplan, Lance M. and Tao, Shu and Yan, Xifeng},
title = {Analyzing Expert Behaviors in Collaborative Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623722},
doi = {10.1145/2623330.2623722},
abstract = {Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. A task is posted and subsequently routed in the network from an expert to another until being resolved. When an expert cannot solve a task, his routing decision (i.e., where to transfer a task) is critical since it can significantly affect the completion time of a task. In this work, we attempt to deduce the cognitive process of task routing, and model the decision making of experts as a generative process where a routing decision is made based on mixed routing patterns.In particular, we observe an interesting phenomenon that an expert tends to transfer a task to someone whose knowledge is neither too similar to nor too different from his own. Based on this observation, an expertise difference based routing pattern is developed. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them. For a held-out set of tasks, our model not only explains their real routing sequences very well, but also accurately predicts their completion time. Under three different quality measures, our method significantly outperforms all the alternatives with more than 75% accuracy gain. In practice, with the help of our model, hypotheses on how to improve a collaborative network can be tested quickly and reliably, thereby significantly easing performance improvement of collaborative networks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1486–1495},
numpages = {10},
keywords = {generative model, collaborative network, task routing, user modeling},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623649,
author = {Yao, Yuan and Tong, Hanghang and Xu, Feng and Lu, Jian},
title = {Predicting Long-Term Impact of CQA Posts: A Comprehensive Viewpoint},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623649},
doi = {10.1145/2623330.2623649},
abstract = {Community Question Answering (CQA) sites have become valuable platforms to create, share, and seek a massive volume of human knowledge. How can we spot an insightful question that would inspire massive further discussions in CQA sites? How can we detect a valuable answer that benefits many users? The long-term impact (e.g., the size of the population a post benefits) of a question/answer post is the key quantity to answer these questions. In this paper, we aim to predict the long-term impact of questions/answers shortly after they are posted in the CQA sites. In particular, we propose a family of algorithms for the prediction problem by modeling three key aspects, i.e., non-linearity, question/answer coupling, and dynamics. We analyze our algorithms in terms of optimality, correctness, and complexity. We conduct extensive experimental evaluations on two real CQA data sets to demonstrate the effectiveness and efficiency of our algorithms.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1496–1505},
numpages = {10},
keywords = {question answering, long-term impact, impact correlation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623752,
author = {Bi, Bin and Kao, Ben and Wan, Chang and Cho, Junghoo},
title = {Who Are Experts Specializing in Landscape Photography? Analyzing Topic-Specific Authority on Content Sharing Services},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623752},
doi = {10.1145/2623330.2623752},
abstract = {With the rapid growth of Web 2.0, a variety of content sharing services, such as Flickr, YouTube, Blogger, and TripAdvisor etc, have become extremely popular over the last decade. On these websites, users have created and shared with each other various kinds of resources, such as photos, video, and travel blogs. The sheer amount of user-generated content varies greatly in quality, which calls for a principled method to identify a set of authorities, who created high-quality resources, from a massive number of contributors of content. Since most previous studies only infer global authoritativeness of a user, there is no way to differentiate the authoritativeness in different aspects of life (topics).In this paper, we propose a novel model of Topic-specific Authority Analysis (TAA), which addresses the limitations of the previous approaches, to identify authorities specific to given query topic(s) on a content sharing service. This model jointly leverages the usage data collected from the sharing log and the favorite log. The parameters in TAA are learned from a constructed training dataset, for which a novel logistic likelihood function is specifically designed. To perform Bayesian inference for TAA with the new logistic likelihood, we extend typical Gibbs sampling by introducing auxiliary variables. Thorough experiments with two real-world datasets demonstrate the effectiveness of TAA in topic-specific authority identification as well as the generalizability of the TAA generative model.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1506–1515},
numpages = {10},
keywords = {topic-specific authority analysis, bayesian model, content sharing services},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630820,
author = {Subramaniam, Sri},
title = {Frontiers in E-Commerce Personalization},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630820},
doi = {10.1145/2623330.2630820},
abstract = {E-commerce has largely been a 'Pull' model to date. Offline retailers have nailed discovery, delight, serendipity, and impulse purchases in person with greater success than online commerce sites. However, in an always-on, mobile-first world, companies like Groupon have the opportunity to push the frontier even further than offline retailers or comprehensive sites due to the fact that our smartphones are always with us. The challenge is to provide the right deals to the right user at the right time. That involves learning about the users and their locations, their personal preferences, and predicting which deals are likely to delight them, presenting diversity, discovery and engaging UX to gather user preferences and semantic graph approaches for user-deal matching. This presentation will give insight into how Groupon manages to grapple with these challenges via a data-driven system in order to delight and surprise customers.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1516},
numpages = {1},
keywords = {recommender systems, e-commerce, mobile technology, geolocation data},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630821,
author = {De Poalo, Tracy and Howard, Jeremy},
title = {Predictive Modeling in Practice: A Case Study from Sprint},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630821},
doi = {10.1145/2623330.2630821},
abstract = {It's not often we get to peer into the details of how big corporates use predictive modeling in practice. In this talk, Sprint's Head of Predictive Modeling, Tracey De Poalo, will talk about the process she developed using SAS and logistic regression to build a wide range of models. Jeremy Howard will discuss his experience as a consultant at Sprint comparing R and random forests to the existing process, and will show the pros and cons of each approach.The talk will also cover the 'real world' issues that Tracey has dealt with in creating a reusable process, including data mart development, sampling, testing, reporting, and implementation with internal customers.Sprint's existing process is the best that Jeremy has seen in industry, and shows a lot of best practices in data structure, documentation, testing, automation, and customer interaction, and modeling.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1517},
numpages = {1},
keywords = {random forests, machine learning, logistic regression},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630822,
author = {Shah, Nigam},
title = {Medicine in the Age of Electronic Health Records},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630822},
doi = {10.1145/2623330.2630822},
abstract = {In the era of EHRs, it is possible to examine the outcomes of decisions made by doctors during clinical practice to identify patterns of care---generating evidence based on the collective practice of experts. We will discuss methods that use unstructured patient data to monitor for adverse drug events, profile specific drugs, identify off-label drug usage, uncover 'natural experiments' and generate practice-based evidence for difficult-to-test clinical hypotheses. We will describe how to detect associations among drugs and their adverse events several years before an alert is issued as well as compute the true rate of drug-drug interactions. We will present approaches to identify novel off-label uses of drugs using the patient feature matrix along with prior knowledge about drugs, diseases, and known usage. We will review a natural experiment--where a subset of congestive heart failure patients who were prescribed Cilostazol despite its black box warning--and profile its safety. We will discuss the testing of a clinical hypothesis about an association between allergic conditions and chronic uveitis in patients with juvenile idiopathic arthritis.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1518},
numpages = {1},
keywords = {clinical data warehouse, learning health system, practice-based evidence, unstructured ehr},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630823,
author = {Rudin, Cynthia},
title = {Algorithms for Interpretable Machine Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630823},
doi = {10.1145/2623330.2630823},
abstract = {It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a clear convincing reason to make a particular prediction.I will discuss recent work on interpretable predictive modeling with decision lists and sparse integer linear models. I will describe several approaches, including an algorithm based on discrete optimization, and an algorithm based on Bayesian analysis. I will show examples of interpretable models for stroke prediction in medical patients and prediction of violent crime in young people raised in out-of-home care.Collaborators are Ben Letham, Berk Ustun, Stefano Traca, Siong Thye Goh, Tyler McCormick, and David Madigan.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1519},
numpages = {1},
keywords = {machine learning, sparsity, interpretability, comprehensibility, medical calculators, understandability},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630824,
author = {Conway, Drew},
title = {Data Science through the Lens of Social Science},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630824},
doi = {10.1145/2623330.2630824},
abstract = {In this talk, Drew will examine data science through the lens of the social scientist. He will discuss how the various skills and disciplines combine into data science. Drew will also present a motivating example directly from his work as a senior advisor to NYC's Mayor's Office of Analytics.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1520},
numpages = {1},
keywords = {local government, data science, social science},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2635636,
author = {Waltzman, Rand},
title = {Information Environment Security},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2635636},
doi = {10.1145/2623330.2635636},
abstract = {The US Department of Defense Dictionary of Military terms defines the Information Environment as 'the aggregate of individuals, organizations, and systems that collect, process, disseminate, or act on information.' The decisions and actions we take both as individuals and collectively simultaneously shape and are shaped by the information environment in which we live. The nature of our interaction with the information environment is rapidly evolving and old models are becoming irrelevant faster than we can develop new ones. This results in uncertainties that leave us exposed to dangerous influences without proper defenses. The purpose of this talk is to help frame a new science of Information Environment Security (IES) whose goal is to create and apply the tools needed to discover and maintain fundamental models of our ever-changing information environment and to defend us in that environment, both as individuals and collectively, against intentional as well as unintentional attempts to deceive, misinform and otherwise manipulate us. IES is an interdisciplinary science that will require bringing together experts working in areas such as cognitive science, computer science, social science, security, marketing, political campaigning, public policy, and psychology to develop a theoretical as well as an applied engineering methodology for managing the full spectrum of information environment security issues.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1521},
numpages = {1},
keywords = {marketing, social science, information environment, cognitive science, security, public policy, psychology, computer science, political campaigning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630819,
author = {Eagle, Nathan},
title = {Big Data for Social Good},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630819},
doi = {10.1145/2623330.2630819},
abstract = {Petabytes of data about human movements, transactions and communication patterns are being generated by everyday technologies such as mobile phones & credit cards. This unprecedented volume of information facilitates a novel set of research questions applicable to a wide range of development issues. In collaboration involving 237 mobile phone operators across 102 countries, Jana's mobile technology platform can instantly poll and compensate 3.48 billion active mobile subscriptions. This talk will discuss how insights gained from living in Kenya became the genesis of a technology company currently working with global clients in over 50 countries, including P&G, Google, Unilever, Danone, General Mills, Nestle, Johnson & Johnson, Microsoft, the World Bank, and the United Nations. After providing an overview of the mobile and social media landscapes in emerging markets, we discuss a system that implements polls & mobile subscription compensation. The presentation will conclude by emphasizing the value of consumer data in underserved and understudied regions of the world.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1522},
numpages = {1},
keywords = {emerging markets, social media, mobile technology, polling systems},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630825,
author = {Munro, Robert},
title = {Bringing Data Science to the Speakers of Every Language},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630825},
doi = {10.1145/2623330.2630825},
abstract = {Speakers of more than 5,000 languages have access to internet and communication technologies. The majority of phones, tablets and computers now ship with language-enabled capabilities like speech-recognition and intelligent auto-correction, and people increasingly interact with data-intensive cloud-based language technologies like search-engines and spam-filters. For both personal and large-scale technologies, the service quality drops or disappears entirely outside of a handful of languages. Speakers of low-resource languages correlate with lower access to healthcare, education and higher vulnerability to disasters. Serving the broadest possible range of languages is crucial to ensuring equitable participation in the global information economy.I will present examples of how natural language processing and distributed human computing are improving the lives of speakers of all the world's languages, in areas including education, disaster-response, health and access to employment. When applying natural language processing to the full diversity of the world's communications, we need to go beyond simple keyword analysis and implement complex technologies that require human-in-the-loop processing to ensure usable accuracy. In recent work where more than a million human judgments were collected on unstructured text and imagery data around natural disasters, I will present observations that debunk recent over-optimistic claims about the utility of social media following disasters. On the positive side, I will share results that show how for-profit technologies are improving people's lives by providing sustainable economic growth opportunities when they support more languages, aligning business objectives with global diversity.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1523},
numpages = {1},
keywords = {healthcare, social development, disaster-response, education, human-computer interaction, natural language processing, employment, crowdsourcing},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623342,
author = {Tamersoy, Acar and Roundy, Kevin and Chau, Duen Horng},
title = {Guilt by Association: Large Scale Malware Detection by Mining File-Relation Graphs},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623342},
doi = {10.1145/2623330.2623342},
abstract = {The increasing sophistication of malicious software calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. We present AESOP, a scalable algorithm that identifies malicious executable files by applying Aesop's moral that "a man is known by the company he keeps." We use a large dataset voluntarily contributed by the members of Norton Community Watch, consisting of partial lists of the files that exist on their machines, to identify close relationships between files that often appear together on machines. AESOP leverages locality-sensitive hashing to measure the strength of these inter-file relationships to construct a graph, on which it performs large scale inference by propagating information from the labeled files (as benign or malicious) to the preponderance of unlabeled files. AESOP attained early labeling of 99% of benign files and 79% of malicious files, over a week before they are labeled by the state-of-the-art techniques, with a 0.9961 true positive rate at flagging malware, at 0.0001 false positive rate.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1524–1533},
numpages = {10},
keywords = {belief propagation, file graph, graph mining, locality sensitive hashing, malware detection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623346,
author = {Kannan, Anitha and Baker, Simon and Ramnath, Krishnan and Fiss, Juliet and Lin, Dahua and Vanderwende, Lucy and Ansary, Rizwan and Kapoor, Ashish and Ke, Qifa and Uyttendaele, Matt and Wang, Xin-Jing and Zhang, Lei},
title = {Mining Text Snippets for Images on the Web},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623346},
doi = {10.1145/2623330.2623346},
abstract = {Images are often used to convey many different concepts or illustrate many different stories. We propose an algorithm to mine multiple diverse, relevant, and interesting text snippets for images on the web. Our algorithm scales to all images on the web. For each image, all webpages that contain it are considered. The top-K text snippet selection problem is posed as combinatorial subset selection with the goal of choosing an optimal set of snippets that maximizes a combination of relevancy, interestingness, and diversity. The relevancy and interestingness are scored by machine learned models. Our algorithm is run at scale on the entire image index of a major search engine resulting in the construction of a database of images with their corresponding text snippets. We validate the quality of the database through a large-scale comparative study. We showcase the utility of the database through two web-scale applications: (a) augmentation of images on the web as webpages are browsed and (b)~an image browsing experience (similar in spirit to web browsing) that is enabled by interconnecting semantically related images (which may not be visually related) through shared concepts in their corresponding text snippets.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1534–1543},
numpages = {10},
keywords = {relevance, diversity, semantic image browsing, text snippets, web image augmentation, browsing, interestingness, text mining for images},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623355,
author = {Tamhane, Ashay and Ikbal, Shajith and Sengupta, Bikram and Duggirala, Mayuri and Appleton, James},
title = {Predicting Student Risks through Longitudinal Analysis},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623355},
doi = {10.1145/2623330.2623355},
abstract = {Poor academic performance in K-12 is often a precursor to unsatisfactory educational outcomes such as dropout, which are associated with significant personal and social costs. Hence, it is important to be able to predict students at risk of poor performance, so that the right personalized intervention plans can be initiated. In this paper, we report on a large-scale study to identify students at risk of not meeting acceptable levels of performance in one state-level and one national standardized assessment in Grade 8 of a major US school district. An important highlight of our study is its scale - both in terms of the number of students included, the number of years and the number of features, which provide a very solid grounding to the research. We report on our experience with handling the scale and complexity of data, and on the relative performance of various machine learning techniques we used for building predictive models. Our results demonstrate that it is possible to predict students at-risk of poor assessment performance with a high degree of accuracy, and to do so well in advance. These insights can be used to pro-actively initiate personalized intervention programs and improve the chances of student success.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1544–1552},
numpages = {9},
keywords = {education, longitudinal data, prediction, analysis, risk},
location = {New York, New York, USA},
series = {KDD '14}
}

@dataset{10.1145/review-2623330.2623355_R50302,
author = {Hughes, Ernest L},
title = {Review ID:R50302 for DOI: 10.1145/2623330.2623355},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/review-2623330.2623355_R50302}
}

@inproceedings{10.1145/2623330.2623367,
author = {Wang, Bingsheng and Xiong, Jinjun},
title = {Novel Geospatial Interpolation Analytics for General Meteorological Measurements},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623367},
doi = {10.1145/2623330.2623367},
abstract = {This paper addresses geospatial interpolation for meteorological measurements in which we estimate the values of climatic metrics at unsampled sites with existing observations. Providing climatological and meteorological conditions covering a large region is potentially useful in many applications, such as smart grid. However, existing research works on interpolation either cause a large number of complex calculations or are lack of high accuracy. We propose a Bayesian compressed sensing based non-parametric statistical model to efficiently perform the spatial interpolation task. Student-t priors are employed to model the sparsity of unknown signals' coefficients, and the Approximated Variational Inference (AVI) method is provided for effective and fast learning. The presented model has been deployed at IBM, targeting for aiding the intelligent management of smart grid. The evaluations on two real world datasets demonstrate that our algorithm achieves state-of-the-art performance in both effectiveness and efficiency.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1553–1562},
numpages = {10},
keywords = {meteorological measurements, geospatial interpolation, smart grid, bayesian inference, analytics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623335,
author = {Abelson, Brian and Varshney, Kush R. and Sun, Joy},
title = {Targeting Direct Cash Transfers to the Extremely Poor},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623335},
doi = {10.1145/2623330.2623335},
abstract = {Unconditional cash transfers to the extreme poor via mobile telephony represent a radical, new approach to giving. GiveDirectly is a non-governmental organization (NGO) at the vanguard of delivering this proven and effective approach to reducing poverty. In this work, we streamline an important step in the operations of the NGO by developing and deploying a data-driven system for locating villages with extreme poverty in Kenya and Uganda. Using the type of roof of a home, thatched or metal, as a proxy for poverty, we develop a new remote sensing approach for selecting extremely poor villages to target for cash transfers. We develop an analytics algorithm that estimates housing quality and density in patches of publicly-available satellite imagery by learning a predictive model with sieves of template matching results combined with color histograms as features. We develop and deploy a crowdsourcing interface to obtain labeled training data. We deploy the predictive model to construct a fine-scale heat map of poverty and integrate this discovered knowledge into the processes of GiveDirectly's operations. Aggregating estimates at the village level, we produce a ranked list from which top villages are included in GiveDirectly's planned distribution of cash transfers. The automated approach increases village selection efficiency significantly.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1563–1572},
numpages = {10},
keywords = {poverty economics, remote sensing, social good},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623349,
author = {Dalessandro, Brian and Chen, Daizhuo and Raeder, Troy and Perlich, Claudia and Han Williams, Melinda and Provost, Foster},
title = {Scalable Hands-Free Transfer Learning for Online Advertising},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623349},
doi = {10.1145/2623330.2623349},
abstract = {Internet display advertising is a critical revenue source for publishers and online content providers, and is supported by massive amounts of user and publisher data. Targeting display ads can be improved substantially with machine learning methods, but building many models on massive data becomes prohibitively expensive computationally. This paper presents a combination of strategies, deployed by the online advertising firm Dstillery, for learning many models from extremely high-dimensional data efficiently and without human intervention. This combination includes: (i)~A method for simple-yet-effective transfer learning where a model learned from data that is relatively abundant and cheap is taken as a prior for Bayesian logistic regression trained with stochastic gradient descent (SGD) from the more expensive target data. (ii)~A new update rule for automatic learning rate adaptation, to support learning from sparse, high-dimensional data, as well as the integration with adaptive regularization. We present an experimental analysis across 100 different ad campaigns, showing that the transfer learning indeed improves performance across a large number of them, especially at the start of the campaigns. The combined "hands-free" method needs no fiddling with the SGD learning rate, and we show that it is just as effective as using expensive grid search to set the regularization parameter for each campaign.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1573–1582},
numpages = {10},
keywords = {online advertising, transfer learning, stochastic gradient descent},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623374,
author = {Luo, Chen and Lou, Jian-Guang and Lin, Qingwei and Fu, Qiang and Ding, Rui and Zhang, Dongmei and Wang, Zhe},
title = {Correlating Events with Time Series for Incident Diagnosis},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623374},
doi = {10.1145/2623330.2623374},
abstract = {As online services have more and more popular, incident diagnosis has emerged as a critical task in minimizing the service downtime and ensuring high quality of the services provided. For most online services, incident diagnosis is mainly conducted by analyzing a large amount of telemetry data collected from the services at runtime. Time series data and event sequence data are two major types of telemetry data. Techniques of correlation analysis are important tools that are widely used by engineers for data-driven incident diagnosis. Despite their importance, there has been little previous work addressing the correlation between two types of heterogeneous data for incident diagnosis: continuous time series data and temporal event data. In this paper, we propose an approach to evaluate the correlation between time series data and event data. Our approach is capable of discovering three important aspects of event-timeseries correlation in the context of incident diagnosis: existence of correlation, temporal order, and monotonic effect. Our experimental results on simulation data sets and two real data sets demonstrate the effectiveness of the algorithm.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1583–1592},
numpages = {10},
keywords = {two-sample problem, incident diagnosis, correlation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623363,
author = {Liu, Chuanren and Ge, Yong and Xiong, Hui and Xiao, Keli and Geng, Wei and Perkins, Matt},
title = {Proactive Workflow Modeling by Stochastic Processes with Application to Healthcare Operation and Management},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623363},
doi = {10.1145/2623330.2623363},
abstract = {Advances in real-time location system (RTLS) solutions have enabled us to collect massive amounts of fine-grained semantically rich location traces, which provide unparalleled opportunities for understanding human activities and discovering useful knowledge. This, in turn, delivers intelligence for real-time decision making in various fields, such as workflow management. Indeed, it is a new paradigm for workflow modeling by the knowledge discovery in location traces. To that end, in this paper, we provide a focused study of workflow modeling by the integrated analysis of indoor location traces in the hospital environment. In comparison with conventional workflow modeling based on passive workflow logs, one salient feature of our approach is that it can proactively unravel the workflow patterns hidden in the location traces, by automatically constructing the workflow states and estimating parameters describing the transition patterns of moving objects. Specifically, to determine a meaningful granularity for the model, the workflow states are first constructed as regions associated with specific healthcare activities. Then, we transform the original indoor location traces to the sequences of workflow states and model the workflow transition patterns by finite state machines. Furthermore, we leverage the correlations in the location traces between related types of medical devices to reinforce the modeling performance and enable more applications. The results show that the proposed framework can not only model the workflow patterns effectively, but also have managerial applications in workflow monitoring, auditing, and inspection of workflow compliance, which are critical in the healthcare industry.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1593–1602},
numpages = {10},
keywords = {healthcare operation and management, workflow modeling, indoor location traces},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623362,
author = {Agarwal, Deepak and Chen, Bee-Chung and Gupta, Rupesh and Hartman, Joshua and He, Qi and Iyer, Anand and Kolar, Sumanth and Ma, Yiming and Shivaswamy, Pannagadatta and Singh, Ajit and Zhang, Liang},
title = {Activity Ranking in LinkedIn Feed},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623362},
doi = {10.1145/2623330.2623362},
abstract = {Users on an online social network site generate a large number of heterogeneous activities, ranging from connecting with other users, to sharing content, to updating their profiles. The set of activities within a user's network neighborhood forms a stream of updates for the user's consumption. In this paper, we report our experience with the problem of ranking activities in the LinkedIn homepage feed. In particular, we provide a taxonomy of social network activities, describe a system architecture (with a number of key components open-sourced) that supports fast iteration in model development, demonstrate a number of key factors for effective ranking, and report experimental results from extensive online bucket tests.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1603–1612},
numpages = {10},
keywords = {activity ranking, relevance, large scale learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623366,
author = {Agarwal, Deepak and Ghosh, Souvik and Wei, Kai and You, Siyu},
title = {Budget Pacing for Targeted Online Advertisements at LinkedIn},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623366},
doi = {10.1145/2623330.2623366},
abstract = {Targeted online advertising is a prime source of revenue for many Internet companies. It is a common industry practice to use a generalized second price auction mechanism to rank advertisements at every opportunity of an impression. This greedy algorithm is suboptimal for both advertisers and publishers when advertisers have a finite budget. In a greedy mechanism high performing advertisers tend to drop out of the auction marketplace fast and that adversely affects both the advertiser experience and the publisher revenue. We describe a method for improving such ad serving systems by including a budget pacing component that serves ads by being aware of global supply patterns. Such a system is beneficial for both advertisers and publishers. We demonstrate the benefits of this component using experiments we conducted on advertising at LinkedIn.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1613–1619},
numpages = {7},
keywords = {budget pacing, generalized second price auction, targeted online advertising},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623339,
author = {Radosavljevik, Dejan and van der Putten, Peter},
title = {Large Scale Predictive Modeling for Micro-Simulation of 3G Air Interface Load},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623339},
doi = {10.1145/2623330.2623339},
abstract = {This paper outlines the approach developed together with the Radio Network Strategy & Design Department of a large European telecom operator in order to forecast the Air-Interface load in their 3G network, which is used for planning network upgrades and budgeting purposes. It is based on large scale intelligent data analysis and modeling at the level of thousands of individual radio cells resulting in 30,000 models per day. It has been embedded into a scenario simulation framework that is used by end users not experienced in data mining for studying and simulating the behavior of this complex networked system, as an example of a systematic approach to the deployment step in the KDD process. This system is already in use for two years in the country where it was developed and it is a part of a standard business process. In the last six months this national operator became a competence center for predictive modeling for micro-simulation of 3G air interface load for four other operators of the same parent company.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1620–1629},
numpages = {10},
keywords = {mobile network, simulation, linear regression, air-interface load},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623360,
author = {Lin, Derek and Raghu, Rashmi and Ramamurthy, Vivek and Yu, Jin and Radhakrishnan, Regunathan and Fernandez, Joseph},
title = {Unveiling Clusters of Events for Alert and Incident Management in Large-Scale Enterprise It},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623360},
doi = {10.1145/2623330.2623360},
abstract = {Large enterprise IT (Information Technology) infrastructure components generate large volumes of alerts and incident tickets. These are manually screened, but it is otherwise difficult to extract information automatically from them to gain insights in order to improve operational efficiency. We propose a framework to cluster alerts and incident tickets based on the text in them, using unsupervised machine learning. This would be a step towards eliminating manual classification of the alerts and incidents, which is very labor intense and costly. Our framework can handle the semi-structured text in alerts generated by IT infrastructure components such as storage devices, network devices, servers etc., as well as the unstructured text in incident tickets created manually by operations support personnel. After text pre-processing and application of appropriate distance metrics, we apply different graph-theoretic approaches to cluster the alerts and incident tickets, based on their semi-structured and unstructured text respectively. For automated interpretation and read-ability on semi-structured text clusters, we propose a method to visualize clusters that preserves the structure and human-readability of the text data as compared to traditional word clouds where the text structure is not preserved; for unstructured text clusters, we find a simple way to define prototypes of clusters for easy interpretation. This framework for clustering and visualization will enable enterprises to prioritize the issues in their IT infrastructure and improve the reliability and availability of their services.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1630–1639},
numpages = {10},
keywords = {hierarchical clustering, complete linkage, alerts and incidents management, kd-tree, connected components, non-negative matrix factorization, graph cut, tickets analysis},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623338,
author = {Hu, Diane J. and Hall, Rob and Attenberg, Josh},
title = {Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-Commerce},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623338},
doi = {10.1145/2623330.2623338},
abstract = {Purchasing decisions in many product categories are heavily influenced by the shopper's aesthetic preferences. It's insufficient to simply match a shopper with popular items from the category in question; a successful shopping experience also identifies products that match those aesthetics. The challenge of capturing shoppers' styles becomes more difficult as the size and diversity of the marketplace increases. At Etsy, an online marketplace for handmade and vintage goods with over 30 million diverse listings, the problem of capturing taste is particularly important -- users come to the site specifically to find items that match their eclectic styles.In this paper, we describe our methods and experiments for deploying two new style-based recommender systems on the Etsy site. We use Latent Dirichlet Allocation (LDA) to discover trending categories and styles on Etsy, which are then used to describe a user's "interest" profile. We also explore hashing methods to perform fast nearest neighbor search on a map-reduce framework, in order to efficiently obtain recommendations. These techniques have been implemented successfully at very large scale, substantially improving many key business metrics.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1640–1649},
numpages = {10},
keywords = {recommender systems, collaborative filtering, topic modeling},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623333,
author = {Junqu\'{e} de Fortuny, Enric and Stankova, Marija and Moeyersoms, Julie and Minnaert, Bart and Provost, Foster and Martens, David},
title = {Corporate Residence Fraud Detection},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623333},
doi = {10.1145/2623330.2623333},
abstract = {With the globalisation of the world's economies and ever-evolving financial structures, fraud has become one of the main dissipaters of government wealth and perhaps even a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor, data availability and high sensitivity have caused this domain to be largely untouched by academia. The current Belgian government has pledged to tackle this issue at large by using a variety of in-house approaches and cooperations with institutions such as academia, the ultimate goal being a fair and efficient taxation system. This is the first data mining application specifically aimed at finding corporate residence fraud, where we show the predictive value of using both structured and fine-grained invoicing data. We further describe the problems involved in building such a fraud detection system, which are mainly data-related (e.g. data asymmetry, quality, volume, variety and velocity) and deployment-related (e.g. the need for explanations of the predictions made).},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1650–1659},
numpages = {10},
keywords = {fraud detection, transactional data, corporate residence fraud, structured data},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623376,
author = {Jin, Fang and Khandpur, Rupinder Paul and Self, Nathan and Dougherty, Edward and Guo, Sheng and Chen, Feng and Prakash, B. Aditya and Ramakrishnan, Naren},
title = {Modeling Mass Protest Adoption in Social Network Communities Using Geometric Brownian Motion},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623376},
doi = {10.1145/2623330.2623376},
abstract = {Modeling the movement of information within social media outlets, like Twitter, is key to understanding to how ideas spread but quantifying such movement runs into several difficulties. Two specific areas that elude a clear characterization are (i) the intrinsic random nature of individuals to potentially adopt and subsequently broadcast a Twitter topic, and (ii) the dissemination of information via non-Twitter sources, such as news outlets and word of mouth, and its impact on Twitter propagation. These distinct yet inter-connected areas must be incorporated to generate a comprehensive model of information diffusion. We propose a bispace model to capture propagation in the union of (exclusively) Twitter and non-Twitter environments. To quantify the stochastic nature of Twitter topic propagation, we combine principles of geometric Brownian motion and traditional network graph theory. We apply Poisson process functions to model information diffusion outside of the Twitter mentions network. We discuss techniques to unify the two sub-models to accurately model information dissemination. We demonstrate the novel application of these techniques on real Twitter datasets related to mass protest adoption in social communities.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1660–1669},
numpages = {10},
keywords = {social networks, geometric brownian motion, information diffusion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623343,
author = {Melli, Gabor},
title = {Shallow Semantic Parsing of Product Offering Titles <i>(for Better Automatic Hyperlink Insertion)</i>},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623343},
doi = {10.1145/2623330.2623343},
abstract = {With billions of database-generated pages on the Web where consumers can readily add priced product offerings to their virtual shopping cart, several opportunities will become possible once we can automatically recognize what exactly is being offered for sale on each page. We present a case study of a deployed data-driven system that first chunks individual titles into semantically classified sub-segments, and then uses this information to improve a hyperlink insertion service.To accomplish this process, we propose an annotation structure that is general enough to apply to offering titles from most e-commerce industries while also being specific enough to identify useful semantics about each offer. To automate the parsing task we apply the best-practices approach of training a supervised conditional random fields model and discover that creating separate prediction models for some of the industries along with the use of model-ensembles achieves the best performance to date.We further report on a real-world application of the trained parser to the task of growing a lexical dictionary of product-related terms which critically provides background knowledge to an affiliate-marketing hyperlink insertion service. On a regular basis we apply the parser to offering titles to produce a large set of labeled terms. From these candidates we select the most confidently predicted novel terms for review by crowd-sourced annotators. The agreed on terms are then added into a dictionary which significantly improves the performance of the link-insertion service. Finally, to continually improve system performance, we retrain the model in an online fashion by performing additional annotations on titles with incorrect predictions on each batch.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1670–1678},
numpages = {9},
keywords = {automated terminology extraction, composite crf ensembles, hyperlink insertion, product offer titles, shallow semantic parsing},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623361,
author = {Acs, Gergely and Castelluccia, Claude},
title = {A Case Study: Privacy Preserving Release of Spatio-Temporal Density in Paris},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623361},
doi = {10.1145/2623330.2623361},
abstract = {With billions of handsets in use worldwide, the quantity of mobility data is gigantic. When aggregated they can help understand complex processes, such as the spread viruses, and built better transportation systems, prevent traffic congestion. While the benefits provided by these datasets are indisputable, they unfortunately pose a considerable threat to location privacy.In this paper, we present a new anonymization scheme to release the spatio-temporal density of Paris, in France, i.e., the number of individuals in 989 different areas of the city released every hour over a whole week. The density is computed from a call-data-record (CDR) dataset, provided by the French Telecom operator Orange, containing the CDR of roughly 2 million users over one week. Our scheme is differential private, and hence, provides provable privacy guarantee to each individual in the dataset. Our main goal with this case study is to show that, even with large dimensional sensitive data, differential privacy can provide practical utility with meaningful privacy guarantee, if the anonymization scheme is carefully designed. This work is part of the national project XData (http://xdata.fr) that aims at combining large (anonymized) datasets provided by different service providers (telecom, electricity, water management, postal service, etc.).},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1679–1688},
numpages = {10},
keywords = {differential privacy, spatio-temporal density, call-data-record (cdr)},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623365,
author = {Herodotou, Herodotos and Ding, Bolin and Balakrishnan, Shobana and Outhred, Geoff and Fitter, Percy},
title = {Scalable near Real-Time Failure Localization of Data Center Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623365},
doi = {10.1145/2623330.2623365},
abstract = {Large-scale data center networks are complex---comprising several thousand network devices and several hundred thousand links---and form the critical infrastructure upon which all higher-level services depend on. Despite the built-in redundancy in data center networks, performance issues and device or link failures in the network can lead to user-perceived service interruptions. Therefore, determining and localizing user-impacting availability and performance issues in the network in near real time is crucial. Traditionally, both passive and active monitoring approaches have been used for failure localization. However, data from passive monitoring is often too noisy and does not effectively capture silent or gray failures, whereas active monitoring is potent in detecting faults but limited in its ability to isolate the exact fault location depending on its scale and granularity.Our key idea is to use statistical data mining techniques on large-scale active monitoring data to determine a ranked list of suspect causes, which we refine with passive monitoring signals. In particular, we compute a failure probability for devices and links in near real time using data from active monitoring, and look for statistically significant increases in the failure probability. We also correlate the probabilistic output with other failure signals from passive monitoring to increase the confidence of the probabilistic analysis. We have implemented our approach in the Windows Azure production environment and have validated its effectiveness in terms of localization accuracy, precision, and time to localization using known network incidents over the past three months. The correlated ranked list of devices and links is surfaced as a report that is used by network operators to investigate current issues and identify probable root causes.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1689–1698},
numpages = {10},
keywords = {data center networks, failure localization},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623364,
author = {Xu, Jian and Wickramarathne, Thanuka L. and Chawla, Nitesh V. and Grey, Erin K. and Steinhaeuser, Karsten and Keller, Reuben P. and Drake, John M. and Lodge, David M.},
title = {Improving Management of Aquatic Invasions by Integrating Shipping Network, Ecological, and Environmental Data: Data Mining for Social Good},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623364},
doi = {10.1145/2623330.2623364},
abstract = {The unintentional transport of invasive species (i.e., non-native and harmful species that adversely affect habitats and native species) through the Global Shipping Network (GSN) causes substantial losses to social and economic welfare (e.g., annual losses due to ship-borne invasions in the Laurentian Great Lakes is estimated to be as high as USD 800 million). Despite the huge negative impacts, management of such invasions remains challenging because of the complex processes that lead to species transport and establishment. Numerous difficulties associated with quantitative risk assessments (e.g., inadequate characterizations of invasion processes, lack of crucial data, large uncertainties associated with available data, etc.) have hampered the usefulness of such estimates in the task of supporting the authorities who are battling to manage invasions with limited resources. We present here an approach for addressing the problem at hand via creative use of computational techniques and multiple data sources, thus illustrating how data mining can be used for solving crucial, yet very complex problems towards social good. By modeling implicit species exchanges as a network that we refer to as the Species Flow Network (SFN), large-scale species flow dynamics are studied via a graph clustering approach that decomposes the SFN into clusters of ports and inter-cluster connections. We then exploit this decomposition to discover crucial knowledge on how patterns in GSN affect aquatic invasions, and then illustrate how such knowledge can be used to devise effective and economical invasive species management strategies. By experimenting on actual GSN traffic data for years 1997-2006, we have discovered crucial knowledge that can significantly aid the management authorities.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1699–1708},
numpages = {10},
keywords = {clustering, data mining for social good, risk assessment, networks, data mining, invasive species},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623369,
author = {Kate, Kiran and Chaudhari, Sneha and Prapanca, Andy and Kalagnanam, Jayant},
title = {FoodSIS: A Text Mining System to Improve the State of Food Safety in Singapore},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623369},
doi = {10.1145/2623330.2623369},
abstract = {Food safety is an important health issue in Singapore as the number of food poisoning cases have increased significantly over the past few decades. The National Environment Agency of Singapore (NEA) is the primary government agency responsible for monitoring and mitigating the food safety risks. In an effort to pro-actively monitor emerging food safety issues and to stay abreast with developments related to food safety in the world, NEA tracks the World Wide Web as a source of news feeds to identify food safety related articles. However, such information gathering is a difficult and time consuming process due to information overload. In this paper, we present FoodSIS, a system for end-to-end web information gathering for food safety. FoodSIS improves efficiency of such focused information gathering process with the use of machine learning techniques to identify and rank relevant content. We discuss the challenges in building such a system and describe how thoughtful system design and recent advances in machine learning provide a framework that synthesizes interactive learning with classification to provide a system that is used in daily operations. We conduct experiments and demonstrate that our classification approach results in improving the efficiency by average 35% compared to a conventional approach and the ranking approach leads to average 16% improvement in elevating the ranks of relevant articles.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1709–1718},
numpages = {10},
keywords = {text classification, ranking, machine learning, semi-supervised, information gathering, learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623348,
author = {Kapoor, Komal and Sun, Mingxuan and Srivastava, Jaideep and Ye, Tao},
title = {A Hazard Based Approach to User Return Time Prediction},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623348},
doi = {10.1145/2623330.2623348},
abstract = {In the competitive environment of the internet, retaining and growing one's user base is of major concern to most web services. Furthermore, the economic model of many web services is allowing free access to most content, and generating revenue through advertising. This unique model requires securing user time on a site rather than the purchase of good which makes it crucially important to create new kinds of metrics and solutions for growth and retention efforts for web services. In this work, we address this problem by proposing a new retention metric for web services by concentrating on the rate of user return. We further apply predictive analysis to the proposed retention metric on a service, as a means for characterizing lost customers. Finally, we set up a simple yet effective framework to evaluate a multitude of factors that contribute to user return. Specifically, we define the problem of return time prediction for free web services. Our solution is based on the Cox's proportional hazard model from survival analysis. The hazard based approach offers several benefits including the ability to work with censored data, to model the dynamics in user return rates, and to easily incorporate different types of covariates in the model. We compare the performance of our hazard based model in predicting the user return time and in categorizing users into buckets based on their predicted return time, against several baseline regression and classification methods and find the hazard based approach to be superior.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1719–1728},
numpages = {10},
keywords = {customer relationship management, hazard based methods, growth and retention, online user behavior},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623337,
author = {Varshney, Kush R. and Chenthamarakshan, Vijil and Fancher, Scott W. and Wang, Jun and Fang, Dongping and Mojsilovi\'{c}, Aleksandra},
title = {Predicting Employee Expertise for Talent Management in the Enterprise},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623337},
doi = {10.1145/2623330.2623337},
abstract = {Strategic planning and talent management in large enterprises composed of knowledge workers requires complete, accurate, and up-to-date representation of the expertise of employees in a form that integrates with business processes. Like other similar organizations operating in dynamic environments, the IBM Corporation strives to maintain such current and correct information, specifically assessments of employees against job roles and skill sets from its expertise taxonomy. In this work, we deploy an analytics-driven solution that infers the expertise of employees through the mining of enterprise and social data that is not specifically generated and collected for expertise inference. We consider job role and specialty prediction and pose them as supervised classification problems. We evaluate a large number of feature sets, predictive models and postprocessing algorithms, and choose a combination for deployment. This expertise analytics system has been deployed for key employee population segments, yielding large reductions in manual effort and the ability to continually and consistently serve up-to-date and accurate data for several business functions. This expertise management system is in the process of being deployed throughout the corporation.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1729–1738},
numpages = {10},
keywords = {human resources, talent planning, expertise assessment, supervised classification, workforce analytics},
location = {New York, New York, USA},
series = {KDD '14}
}

@dataset{10.1145/review-2623330.2623337_R51256,
author = {Obermeier, Klaus K.},
title = {Review ID:R51256 for DOI: 10.1145/2623330.2623337},
publisher = {Association for Computing Machinery},
url = {https://doi.org/10.1145/review-2623330.2623337_R51256}
}

@inproceedings{10.1145/2623330.2623347,
author = {Zheng, Li and Zeng, Chunqiu and Li, Lei and Jiang, Yexi and Xue, Wei and Li, Jingxuan and Shen, Chao and Zhou, Wubai and Li, Hongtai and Tang, Liang and Li, Tao and Duan, Bing and Lei, Ming and Wang, Pengnian},
title = {Applying Data Mining Techniques to Address Critical Process Optimization Needs in Advanced Manufacturing},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623347},
doi = {10.1145/2623330.2623347},
abstract = {Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.In this paper, we design, implement and deploy an integrated solution, named PDP-Miner, which is a data analytics platform customized for process optimization in Plasma Display Panel (PDP) manufacturing. The system utilizes the latest advances in data mining technologies and Big Data infrastructures to create a complete analytical solution. Besides, our proposed system is capable of supporting automatically configuring and scheduling analysis tasks, and balancing heterogeneous computing resources. The system and the analytic strategies can be applied to other advanced manufacturing fields to enable complex data analysis tasks. Since 2013, PDP-Miner has been deployed as the data analysis platform of ChangHong COC. By taking the advantages of our system, the overall PDP yield rate has increased from 91% to 94%. The monthly production is boosted by 10,000 panels, which brings more than 117 million RMB of revenue improvement per year.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1739–1748},
numpages = {10},
keywords = {process optimization, data mining platform, big data, advanced manufacturing},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623358,
author = {Avvenuti, Marco and Cresci, Stefano and Marchetti, Andrea and Meletti, Carlo and Tesconi, Maurizio},
title = {EARS (Earthquake Alert and Report System): A Real Time Decision Support System for Earthquake Crisis Management},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623358},
doi = {10.1145/2623330.2623358},
abstract = {Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by INGV or by other official channels. Thus, we are able to alert interested parties promptly. Information discovered by our system can be extremely useful to all the government agencies interested in mitigating the impact of earthquakes, as well as the news agencies looking for fresh information to publish.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1749–1758},
numpages = {10},
keywords = {social mining, disaster management, social sensing, event detection, decision support system},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623354,
author = {Der, Matthew F. and Saul, Lawrence K. and Savage, Stefan and Voelker, Geoffrey M.},
title = {Knock It off: Profiling the Online Storefronts of Counterfeit Merchandise},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623354},
doi = {10.1145/2623330.2623354},
abstract = {We describe an automated system for the large-scale monitoring of Web sites that serve as online storefronts for spam-advertised goods. Our system is developed from an extensive crawl of black-market Web sites that deal in illegal pharmaceuticals, replica luxury goods, and counterfeit software. The operational goal of the system is to identify the affiliate programs of online merchants behind these Web sites; the system itself is part of a larger effort to improve the tracking and targeting of these affiliate programs. There are two main challenges in this domain. The first is that appearances can be deceiving: Web pages that render very differently are often linked to the same affiliate program of merchants. The second is the difficulty of acquiring training data: the manual labeling of Web pages, though necessary to some degree, is a laborious and time-consuming process. Our approach in this paper is to extract features that reveal when Web pages linked to the same affiliate program share a similar underlying structure. Using these features, which are mined from a small initial seed of labeled data, we are able to profile the Web sites of forty-four distinct affiliate programs that account, collectively, for hundreds of millions of dollars in illicit e-commerce. Our work also highlights several broad challenges that arise in the large-scale, empirical study of malicious activity on the Web.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1759–1768},
numpages = {10},
keywords = {web page classification, email spam},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623344,
author = {Bendersky, Michael and Garcia-Pueyo, Lluis and Harmsen, Jeremiah and Josifovski, Vanja and Lepikhin, Dima},
title = {Up next: Retrieval Methods for Large Scale Related Video Suggestion},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623344},
doi = {10.1145/2623330.2623344},
abstract = {The explosive growth in sharing and consumption of the video content on the web creates a unique opportunity for scientific advances in video retrieval, recommendation and discovery. In this paper, we focus on the task of video suggestion, commonly found in many online applications. The current state-of-the-art video suggestion techniques are based on the collaborative filtering analysis, and suggest videos that are likely to be co-viewed with the watched video. In this paper, we propose augmenting the collaborative filtering analysis with the topical representation of the video content to suggest related videos. We propose two novel methods for topical video representation. The first method uses information retrieval heuristics such as tf-idf, while the second method learns the optimal topical representations based on the implicit user feedback available in the online scenario. We conduct a large scale live experiment on YouTube traffic, and demonstrate that augmenting collaborative filtering with topical representations significantly improves the quality of the related video suggestions in a live setting, especially for categories with fresh and topically-rich video content such as news videos. In addition, we show that employing user feedback for learning the optimal topical video representations can increase the user engagement by more than 80% over the standard information retrieval representation, when compared to the collaborative filtering baseline.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1769–1778},
numpages = {10},
keywords = {video representation, video retrieval, related video suggestion},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623352,
author = {Xue, Mingqiang and Wu, Huayu and Chen, Wei and Ng, Wee Siong and Goh, Gin Howe},
title = {Identifying Tourists from Public Transport Commuters},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623352},
doi = {10.1145/2623330.2623352},
abstract = {Tourism industry has become a key economic driver for Singapore. Understanding the behaviors of tourists is very important for the government and private sectors, e.g., restaurants, hotels and advertising companies, to improve their existing services or create new business opportunities. In this joint work with Singapore's Land Transport Authority (LTA), we innovatively apply machine learning techniques to identity the tourists among public commuters using the public transportation data provided by LTA. On successful identification, the travelling patterns of tourists are then revealed and thus allow further analyses to be carried out such as on their favorite destinations, region of stay, etc. Technically, we model the tourists identification as a classification problem, and design an iterative learning algorithm to perform inference with limited prior knowledge and labeled data. We show the superiority of our algorithm with performance evaluation and comparison with other state-of-the-art learning algorithms. Further, we build an interactive web-based system for answering queries regarding the moving patterns of the tourists, which can be used by stakeholders to gain insight into tourists' travelling behaviors in Singapore.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1779–1788},
numpages = {10},
keywords = {tourists, public transport, ez-link, data analytics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623353,
author = {Tayebi, Mohammad A. and Ester, Martin and Gl\"{a}sser, Uwe and Brantingham, Patricia L.},
title = {Spatially Embedded Co-Offence Prediction Using Supervised Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623353},
doi = {10.1145/2623330.2623353},
abstract = {Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1789–1798},
numpages = {10},
keywords = {link prediction, co-offence prediction, social network},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623373,
author = {Ramakrishnan, Naren and Butler, Patrick and Muthiah, Sathappan and Self, Nathan and Khandpur, Rupinder and Saraf, Parang and Wang, Wei and Cadena, Jose and Vullikanti, Anil and Korkmaz, Gizem and Kuhlman, Chris and Marathe, Achla and Zhao, Liang and Hua, Ting and Chen, Feng and Lu, Chang Tien and Huang, Bert and Srinivasan, Aravind and Trinh, Khoa and Getoor, Lise and Katz, Graham and Doyle, Andy and Ackermann, Chris and Zavorin, Ilya and Ford, Jim and Summers, Kristen and Fayed, Youssef and Arredondo, Jaime and Gupta, Dipak and Mares, David},
title = { 'Beating the News' with EMBERS: Forecasting Civil Unrest Using Open Source Indicators},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623373},
doi = {10.1145/2623330.2623373},
abstract = {We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1799–1808},
numpages = {10},
keywords = {event forecasting, open source indicators, civil unrest},
location =

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr