text:
@inproceedings{10.1109/ASONAM.2011.129,
title = {Cover Art},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.129},
doi = {10.1109/ASONAM.2011.129},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {C1},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.1,
title = {Title Page i},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.1},
doi = {10.1109/ASONAM.2011.1},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {i},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.2,
title = {Title Page Iii},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.2},
doi = {10.1109/ASONAM.2011.2},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {iii},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.3,
title = {Copyright Page},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.3},
doi = {10.1109/ASONAM.2011.3},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {iv},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.4,
title = {Message from the General Chairs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.4},
doi = {10.1109/ASONAM.2011.4},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {xv–xvi},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.5,
title = {Message from the Program Chairs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.5},
doi = {10.1109/ASONAM.2011.5},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {xvii–xviii},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.126,
title = {Conference Sponsors},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.126},
doi = {10.1109/ASONAM.2011.126},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {xix–xxi},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.9,
title = {External Reviewers},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.9},
doi = {10.1109/ASONAM.2011.9},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {xxviii},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.7,
title = {Invited Keynote Speakers},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.7},
doi = {10.1109/ASONAM.2011.7},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {xxix},
numpages = {1},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.114,
author = {Magnani, Matteo and Rossi, Luca},
title = {The ML-Model for Multi-Layer Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.114},
doi = {10.1109/ASONAM.2011.114},
abstract = {In this paper we introduce a new model to represent an interconnected network of networks. This model is fundamental to reason about the real organization of on-line social networks, where users belong to and interact on different networks at the same time. In addition we extend traditional SNA measures to deal with this multiplicity of networks and we apply the model to a real dataset extracted from two microblogging sites.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {5–12},
numpages = {8},
keywords = {centrality, Multi-layer networks, Social Network Analysis, Social Network Sites},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.58,
author = {Yamaguchi, Yuto and Amagasa, Toshiyuki and Kitagawa, Hiroyuki},
title = {Tag-Based User Topic Discovery Using Twitter Lists},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.58},
doi = {10.1109/ASONAM.2011.58},
abstract = {In this paper, we address the problem of tagging users in Twitter, one of the most popular micro-blogging services. There are growing needs to get useful information from Twitter, because an enormous amount of information is transmitted in real time. Twitter users, who play an important role as information sources, typically transmit information about some particular topics which they are interested in. Therefore, to identify useful information, it is very important to know which topics a user tends to transmit. In this paper, we propose a method to discover appropriate topics for a user by using Twitter list. Twitter list is an official functionality to make a"user list, " list members tend to transmit information about the topic represented in the name of the list. From this observation, our idea is to extract tags from list names, and exploit the relationship among lists, tags extracted from the list names, and list members. Experimental results show the effectiveness of the proposed method.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {13–20},
numpages = {8},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.61,
author = {Macropol, Kathy and Singh, Ambuj},
title = {Content-Based Modeling and Prediction of Information Dissemination},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.61},
doi = {10.1109/ASONAM.2011.61},
abstract = {Social and communication networks across the world generate vast amounts of graph-like data each day. The modeling and prediction of how these communication structures evolve can be highly useful for many applications. Previous research in this area has focused largely on using past graph structure to predict future links. However, a useful observation is that many graph datasets have additional information associated with them beyond just their graph structure. In particular, communication graphs (such as email, twitter, blog graphs, etc.) have information content associated with their graph edges. In this paper we examine the link between information content and graph structure, proposing a new graph modeling approach, GC-Model, which combines both. We then apply this model to multiple real world communication graphs, demonstrating that the built models can be used effectively to predict future graph structure and information flow. On average, GC-Model's top predictions covered 19% more of the actual future graph communication structure when compared to other previously introduced algorithms, far outperforming multiple link prediction methods and several naive approaches.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {21–28},
numpages = {8},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.117,
author = {Danilevsky, Marina and Hailpern, Joshua and Han, Jiawei},
title = {SCENE: Structural Conversation Evolution NEtwork},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.117},
doi = {10.1109/ASONAM.2011.117},
abstract = {It's not just what you say, but it is how you say it. To date, the majority of the Instant Message (IM) analysis and research has focused on the content of the conversation. The main research question has been, `what do people talk about?' focusing on topic extraction and topic modeling. While content is clearly critical for many real-world applications, we have largely ignored identifying `how' people communicate. Conversation structure and communication patterns provide deep insight into how conversations evolve, and how the content is shared. Motivated by theoretical work from psychology and linguistics in the area of conversation alignment, we introduce SCENE, an evolution network approach to extract knowledge from a conversation network. We demonstrate the potential of our approach by taking the task of matching conversation partners. We find that SCENE is more successful because, in contrast to existing approaches, SCENE treats a conversation as an evolving, rather than a static document, and focuses on the structural elements of the conversation instead of being tied to the specific content.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {29–36},
numpages = {8},
keywords = {conversation structure network, conversation evolution network, conversation network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.60,
author = {Ke, Qing and Dong, Yuxiao and Wu, Bin},
title = {Efficient Search in Networks Using Conductance},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.60},
doi = {10.1109/ASONAM.2011.60},
abstract = {Decentralized search in networks is an important algorithmic problem in the study of complex networks and social networks analysis. It has a large number of practical applications, from shortest paths search in social network relationship, web pages search in WWW to querying files in peer-to-peer file sharing networks and so on. In this paper, we explore this problem from a perspective of community structure. We first find that through maximizing sample conductance, we can get high coverage sample. Based on this result, then, we propose a new decentralized search strategy named Conductance Search which tries to efficiently find the nodes belonging to different communities. We compare the strategy with other common strategies. And the results show that the conductance search outperforms others in number of steps to find the target and time complexity. Finally, we find some previous conclusions fail in many real-world networks and discuss network search-ability from the perspective of various structural properties.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {37–44},
numpages = {8},
keywords = {Decentralized Search Algorithm, Social Network Analysis, Community Structure, Conductance},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.102,
author = {Thovex, Christophe and Trichet, Francky},
title = {A Semantic and Multidisciplinary Model for Professional and Social Networks Analysis},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.102},
doi = {10.1109/ASONAM.2011.102},
abstract = {By bridge-building between the classical models of social networks analysis, ontologies engineering and physics, our work defines a multidisciplinary model of professional social networks analysis, dedicated to human and social capital management in enterprises and institutions. We introduce a semantic process of social graphs static and dynamic analysis, based on the enterprise content and producing decisional tools for the performance management. This approach is in line with the Socio prise project, in partnership with a leader software engineering company for human capital management. Socio prise is funded by the French State Secretariat at the prospective and development of digital economy.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {45–52},
numpages = {8},
keywords = {sociophysics, dynamic, decisional, ontology, static, social, networks, analysis, semantic},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.52,
author = {Varlamis, Iraklis and Tsatsaronis, George},
title = {Visualizing Bibliographic Databases as Graphs and Mining Potential Research Synergies},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.52},
doi = {10.1109/ASONAM.2011.52},
abstract = {Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analyzed across different dimensions(e.g., author, year, venue, topic) and can be exploited in multiple ways. The representation and visualization of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualization tool from the biological domain, we are able to provide comprehensive visualizations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {53–60},
numpages = {8},
keywords = {Authors' Similarity, Research Synergies, Power Graph Analysis, Visualizing Bibliographical Databases, Mining Co-authorship Graphs},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.12,
author = {Gupta, Manish and Aggarwal, Charu C. and Han, Jiawei and Sun, Yizhou},
title = {Evolutionary Clustering and Analysis of Bibliographic Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.12},
doi = {10.1109/ASONAM.2011.12},
abstract = {In this paper, we study the problem of evolutionary clustering of multi-typed objects in a heterogeneous bibliographic network. The traditional methods of homogeneous clustering methods do not result in a good typed-clustering. The design of heterogeneous methods for clustering can help us better understand the evolution of each of the types apart from the evolution of the network as a whole. In fact, the problem of clustering and evolution diagnosis are closely related because of the ability of the clustering process to summarize the network and provide insights into the changes in the objects over time. We present such a tightly integrated method for clustering and evolution diagnosis of heterogeneous bibliographic information networks. We present an algorithm, ENetClus, which performs such an agglomerative evolutionary clustering which is able to show variations in the clusters over time with a temporal smoothness approach. Previous work on clustering networks is either based on homogeneous graphs with evolution, or it does not account for evolution in the process of clustering heterogeneous networks. This paper provides the first framework for evolution-sensitive clustering and diagnosis of heterogeneous information networks. The ENetClus algorithm generates consistent typed clusterings across time, which can be used for further evolution diagnosis and insights. The framework of the algorithm is specifically designed in order to facilitate insights about the evolution process. We use this technique in order to provide novel insights about bibliographic information networks.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {63–70},
numpages = {8},
keywords = {evolutionary agglomerative heterogeneous clustering, heterogeneous information networks, bibliographic networks, evolutionary metrics, ENetClus},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.53,
author = {Ye, Qi and Wu, Bin and Zhao, Zhixiong and Wang, Bai},
title = {Detecting Link Communities in Massive Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.53},
doi = {10.1109/ASONAM.2011.53},
abstract = {Most of the existing literature which has entirely focused on clustering nodes in large-scale networks. To discover multi-scale overlapping communities quickly, we propose a highly efficient multi-resolution link community detection algorithm to detect the link communities in massive networks based on the idea of edge labeling. First, we will get the node partition of the network based on a new multi-resolution node detection algorithm. After that, we can find the link community in a linear time by the labels of nodes. Its time complexity is near linear and its space complexity is linear. The effectiveness of our algorithm is demonstrated by extensive experiments on lots of computer generated artificial graphs and real-world networks. The results show that our algorithm is very fast and highly reliable. Tests on real and artificial networks also give excellent results comparing with the newly proposed link partition algorithm.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {71–78},
numpages = {8},
keywords = {Link Community, Graph Mining, Complex Networks, Community Detection},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.48,
author = {Yang, Bo and Huang, Jing and Liu, Dayou},
title = {A Random Network Ensemble Model Based Generalized Network Community Mining Algorithm},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.48},
doi = {10.1109/ASONAM.2011.48},
abstract = {The ability to discover community structures from explorative networks is useful for many applications. Most of the existing methods with regard to community mining are specifically designed for assortative networks, and some of them could be applied to address disassortative networks by means of intentionally modifying the objectives to be optimized. However, the types of the explorative networks are unknown beforehand. Consequently, it is difficult to determine what specific algorithms should be used to mine appropriate structures from exploratory networks. To address this issue, a novel concept, generalized community structure, has been proposed with the attempt to unify the two distinct counterparts in both types of networks. Furthermore, based on the proposed random network ensemble model, a generalized community mining algorithm, so called G-NCMA, has been proposed, which is promisingly suitable for both types of networks. Its performance has been rigorously tested, validated and compared with other related algorithms against real-world networks as well as synthetic networks. Experimental results show the G-NCMA algorithm is able to detect communities, without any prior, from explorative networks with a good accuracy.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {79–86},
numpages = {8},
keywords = {community mining, assortative, random network, disassortative, complex network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.65,
author = {Giatsidis, Christos and Thilikos, Dimitrios M. and Vazirgiannis, Michalis},
title = {Evaluating Cooperation in Communities with the K-Core Structure},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.65},
doi = {10.1109/ASONAM.2011.65},
abstract = {Community sub graphs are characterized by dense connections or interactions among its nodes. Community detection and evaluation is an important task in graph mining. A variety of measures have been proposed to evaluate the quality of such communities. In this paper, we evaluate communities based on the k-core concept, as means of evaluating their collaborative nature - a property not captured by the single node metrics or by the established community evaluation metrics. Based on the k-core, which essentially measures the robustness of a community under degeneracy, we extend it to weighted graphs, devising a novel concept of k-cores on weighted graphs. We applied the k-core approach on large real world graphs--such as DBLP and report interesting results.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {87–93},
numpages = {7},
keywords = {co-authorship graphs, k-core, graph mining, Community evaluation},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.45,
author = {Chua, Freddy Chong Tat and Lim, Ee-Peng},
title = {Modeling Bipartite Graphs Using Hierarchical Structures},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.45},
doi = {10.1109/ASONAM.2011.45},
abstract = {Bipartite networks are often used to capture the relationships between different classes of objects. To model the structure of bipartite networks, we propose a new hierarchical model based on a hierarchical random graph model originally designed for one-mode networks. The new model can better preserve the network fidelity as well as the assortative and disassortative structures of bipartite networks. We apply the proposed model on some paper-author networks in DBLP to find their optimal hierarchical structures. Using the optimal bipartite hierarchical structure, we regenerate networks that exhibit the similar network properties and degree distribution as the observed networks.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {94–101},
numpages = {8},
keywords = {Social Networks, Dendrogram, Bipartite Graphs},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.36,
author = {Reid, Fergal and McDaid, Aaron and Hurley, Neil},
title = {Partitioning Breaks Communities},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.36},
doi = {10.1109/ASONAM.2011.36},
abstract = {Considering a clique as a conservative definition of community structure, we examine how graph partitioning algorithms interact with cliques. Many popular community-finding algorithms partition the entire graph into non-overlapping communities. We show that on a wide range of empirical networks, from different domains, significant numbers of cliques are split across separate partitions, as produced by such algorithms. We examine the largest connected component of the sub graph formed by retaining only edges in cliques, and apply partitioning strategies that explicitly minimise the number of cliques split. We conclude that, due to the connectedness of many networks, any community finding algorithm that produces partitions must fail to find at least some significant structures. Moreover, contrary to traditional intuition, in some empirical networks, strong ties and cliques frequently do cross community boundaries.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {102–109},
numpages = {8},
keywords = {Graph, Community Finding Algorithm, Structure, Clique, Benchmark, Social Network, Partition, Network, Hypergraph},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.77,
author = {Huang, Liang-Cheng and Yen, Tso-Jung and Chou, Seng-Cho T.},
title = {Community Detection in Dynamic Social Networks: A Random Walk Approach},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.77},
doi = {10.1109/ASONAM.2011.77},
abstract = {This study aims to tackling community detection problems in dynamic social networks. The main approach focuses on exploring the idea of random walk in formulating modularity functions for community detection. Under this approach, a modularity function is defined as the difference between the probability of a Markov chain induced by a community and the probability of a null model that assumes no detectable community structure exists in the network. In this paper, we demonstrate the modularity-based approach by applying it to identify group boundaries in an adolescence friendship networks spanning a period of five months. Results and future directions will be discussed.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {110–117},
numpages = {8},
keywords = {Modularity function, Dynamic networks, Community detection, Random walk},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.112,
author = {Sun, Yizhou and Barber, Rick and Gupta, Manish and Aggarwal, Charu C. and Han, Jiawei},
title = {Co-Author Relationship Prediction in Heterogeneous Bibliographic Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.112},
doi = {10.1109/ASONAM.2011.112},
abstract = {The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, i.e., only one type of objects(author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology calledemph{Path Predict}, i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {121–128},
numpages = {8},
keywords = {heterogeneous information networks, relationship prediction, link prediction},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.92,
author = {Barbieri, Nicola},
title = {Regularized Gibbs Sampling for User Profiling with Soft Constraints},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.92},
doi = {10.1109/ASONAM.2011.92},
abstract = {In this paper we extend the formulation of the User Rating Profile model, providing a Gibbs Sampling derivation for parameter estimation. Validation tests on Movielens data show that the proposed approach outperforms significantly the variational version in terms of both prediction accuracy and learning time. Gibbs Sampling provides a simple and flexible learning procedure which can be extended to include external evidence, in the form of soft constraints. More specifically, given a-priori information about user-neighbors, we propose an effective regularization technique that drives the first sampling iterations pushing the model towards a state which better represents the user-neighborhoods specified in input.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {129–136},
numpages = {8},
keywords = {Recommendations, Collaborative Filtering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.56,
author = {Wermser, Hendrik and Rettinger, Achim and Tresp, Volker},
title = {Modeling and Learning Context-Aware Recommendation Scenarios Using Tensor Decomposition},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.56},
doi = {10.1109/ASONAM.2011.56},
abstract = {The task of recommending items, like movies, to users is a core feature of many social networks. Standard approaches either use item or user similarity to suggest the next items users might be interested in. Recently, multivariate models like matrix factorization have become popular to combine the advantages of both perspectives. In addition, extensions have been proposed to capture the dynamics of user interests over time, like trends or recurrent user needs. While offering good predictive performance, so far those models do not exploit possibly available rich semantic context. Typically, only one implicit feature, like user ratings, is tracked to give personalized recommendations. However, with semantic data sources, like linked data, wealthy background knowledge becomes available that could be leveraged to improve predictive performance. We argue, that a more flexible framework is needed to model and learn a greater class of recommendation scenarios where rich context is available. Thus, we propose a generic approach which generalizes state-of-the-art methods based on pair wise interaction tensor factorization by leveraging arbitrary background knowledge related to the recommendation situation. Our experiments on streamed semantic data from a social network show that by adding varying sets of context - like user information, sequential information or time information - the ranking of potential items can be personalized and the predictive performance can be improved.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {137–144},
numpages = {8},
keywords = {Recommender Systems, Tensor Decomposition, Collaborative Filtering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.54,
author = {Liu, Jyi-Shane and Ning, Ke-Chih},
title = {Applying Link Prediction to Ranking Candidates for High-Level Government Post},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.54},
doi = {10.1109/ASONAM.2011.54},
abstract = {The main focus of this study is the computational evaluation of candidacy for an executive vacancy. We identified a new problem framework on bureaucratic promotion and proposed to tackle the problem with social network analysis that involved bipartite graph and link prediction. A bureaucratic career bipartite network model was developed to encode key information reflecting a candidate's service merit and the aggregated merit standards of an executive position. This allowed us to approximate merit measurement with node similarity. We implemented this candidacy evaluation approach and conducted experiments with data from Taiwan's bureaucratic career database. Empirical evaluation shows acceptable baseline performance and demonstrates feasibility of the link prediction approach to candidacy ranking. The results also seem to indicate that bureaucratic promotion for executive positions in Taiwan government is mostly a merit system, as opposed to at-will.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {145–152},
numpages = {8},
keywords = {candidacy ranking, bureaucratic executive promotion, link prediction},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.108,
author = {Kapron, Bruce and Srivastava, Gautam and Venkatesh, S.},
title = {Social Network Anonymization via Edge Addition},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.108},
doi = {10.1109/ASONAM.2011.108},
abstract = {The growing need to address privacy concerns when social network data is released for mining purposes has recently led to considerable interest in various techniques for graph anonymization. In this paper, we study the following problem: Given a social network modeled as an {em edge-labeled graph} $G$, we aim to make a pre-specified subset of vertices of $G$ $k$-label sequence anonymous with the minimum number of edge additions. Here, the label sequence of a vertex is the sequence of labels of edges incident to it. The contributions of this paper are two fold: We provide a framework to show hardness results for different variants of social network anonymization using a common approach. We start by showing that $k$-label sequence anonymity of arbitrary labeled graphs is hard, and use this result to prove NP-hardness results for many other recently proposed notions of graph anonymization. Secondly, we present interesting algorithms and hardness for bipartite graphs. For unlabeled bipartite graphs, we show $k$-degree anonymity is in P for all $k geq 2$. For labeled bipartite graphs, we show that $k$-label sequence anonymity is in P for $k=2$ but it is NP-hard for $k geq 3$.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {155–162},
numpages = {8},
keywords = {algorithms, anonymization, complexity, data privacy, social networks},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.14,
author = {Barbian, Guido},
title = {Assessing Trust by Disclosure in Online Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.14},
doi = {10.1109/ASONAM.2011.14},
abstract = {Knowing about trust between members of an online social network (OSN) is essential for many applications. In this paper we propose and discuss methods for deriving information about trust within a social network by analyzing disclosure of personal information items. A formal model of trust and disclosure is presented and possible trust functions were analysed. We distinguish different types of information and assess their privacy value. A methodology for calculating privacy values and trust functions is provided.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {163–170},
numpages = {8},
keywords = {trust, disclosure, privacy, online social network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.120,
author = {Tayebi, Mohammad A. and Bakker, Laurens and Glasser, Uwe and Dabbaghian, Vahid},
title = {Locating Central Actors in Co-Offending Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.120},
doi = {10.1109/ASONAM.2011.120},
abstract = {A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies for crime prevention and reduction. One of the important tasks in co-offending network analysis is central actors identification. In this paper, firstly we introduce a data model, called unified crime data model to bridge the conceptual gap between abstract crime data level and co-offending network mining level. Using this data model, we extract the co-offending network of five years real-world crime data. Then we apply different variations of centrality methods on the extracted network and discuss how key player identification and removal can help law enforcement agencies in policy making for crime reduction.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {171–179},
numpages = {9},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.81,
author = {Yang, Hsin-Chang and Lee, Chung-Hong},
title = {Post-Level Spam Detection for Social Bookmarking Web Sites},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.81},
doi = {10.1109/ASONAM.2011.81},
abstract = {Social book marking Web sites have emerged recently for collecting and sharing of interesting Web sites among users. People can add Web pages to such sites as bookmarks and allow themselves as well as others to manipulate them. One of the key features of the social book marking sites is the ability of annotating a Web page when it is being bookmarked. The annotation usually contains a set of words or phrases, which are collectively known as tags, that could reveal the semantics of the annotated Web page. Efficient and effective search of Web pages can then be achieved via such tags. However, spam tags that are irrelevant to the content of Web pages often appear to deceive other users for malicious or commercial purposes. Various techniques have been devised to tackle such tag spam detection problem. Most of these techniques were able to detect a user that always annotate spam tags. However, finer levels of detection are seldom discussed. In this work, we will propose a method based on a text mining approach to discover the relations between Web pages and there tag posts. These relations are then used to compute the similarity between a Web page and its tag post to decide if it is a spam post. Preliminary experiments show that the accuracy of the post-level spam detection task is 83%.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {180–185},
numpages = {6},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.49,
author = {Wang, Jenq-Haur and Lin, Ming-Sheng},
title = {Using Inter-Comment Similarity for Comment Spam Detection in Chinese Blogs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.49},
doi = {10.1109/ASONAM.2011.49},
abstract = {Blog has become one of the most popular ways of communication among social communities since blog posts can be replied, commented, and even shared to other users in a convenient way. All posts and comments, no matter good or bad, have to be manually coordinated by blog owners. In order to prevent comment spam, most blog sites provide challenge-response tests such as CAPTCHA to ensure that the response is from human, instead of automatically generated by a computer. However, these tests cannot prohibit spammers from manually leaving spam messages. Existing studies of Chinese blog comment spam only focus on comments containing hyperlinks, which only stand for a small portion of blog comment spam. In this paper, we propose to include inter-comment Jaccard similarity in the features in addition to the post-comment similarity, stop words ratio, and comment length for blog comment classification. In order to verify the effects of inter-comment similarity features, we compared several classification algorithms such as C4.5, Na\"{\i}ve Bayes, and Neural Network. Experimental results showed that the feature combination of inter-comment and post-comment similarity under the classification of C4.5 achieves the best performance. This shows the effectiveness of the proposed inter-comment similarity feature for Chinese blog comment spam classification.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {189–194},
numpages = {6},
keywords = {short document classification, comment spam detection, inter-comment similarity, blog comment},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.86,
author = {Lee, Ruaylong and Nia, Roozbeh and Hsu, Jason and Levitt, Karl N. and Rowe, Jeff and Wu, S. Felix and Ye, Shaozhi},
title = {Design and Implementation of FAITH, An Experimental System to Intercept and Manipulate Online Social Informatics},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.86},
doi = {10.1109/ASONAM.2011.86},
abstract = {Social informatics is the core of Facebook's business and is its most valuable asset which consists of the social graph and the private data of over 500 million users. However, without secure methods of managing this data, Face book has become vulnerable to privacy risks and devaluation. In Facebook's model, users are asked upon access to grant applications the required permissions without sufficient knowledge of the applications' intentions. As a result, if they are deceived, users risk the exposure of sensitive and personal data. This paper presents a system dubbed FAITH (Face book Applications: Identification, Transformation & Hyper visor) to mitigate or eliminate these issues by enhancing the management of social data. First, FAITH allows users to adjust the visibility of their social informatics for each individual application depending on how much they trust the application. Users can configure FAITH to let non-trusted applications run with the least privileges (least amount of social informatics) to minimize potential privacy leaks. Second, FAITH logs the activities of applications to assist users in making more secure decisions. Users can closely monitor each activity performed by applications to adjust their privacy settings more securely. Third, FAITH allows users to transform their social graph such that different applications see different social graphs preventing the formation of friendship inflation caused by applications. The implementation of FAITH only needs the resources and tools available to the public by Face book and requires no further cooperation from the social network. FAITH is a prototype system: the design and concept can be extended to secure other OSNs (Online Social Networks). Currently, FAITH contains thirteen Face book social applications and has been officially released for public usage with approximately two hundred monthly active users as of now.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {195–202},
numpages = {8},
keywords = {privacy protection, Facebook applications, social network sites, Facebook},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.80,
author = {Maruhashi, Koji and Guo, Fan and Faloutsos, Christos},
title = {MultiAspectForensics: Pattern Mining on Large-Scale Heterogeneous Networks with Tensor Analysis},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.80},
doi = {10.1109/ASONAM.2011.80},
abstract = {Modern applications such as web knowledge base, network traffic monitoring and online social networks have made available an unprecedented amount of network data with rich types of interactions carrying multiple attributes, for instance, port number and time tick in the case of network traffic. The design of algorithms to leverage this structured relationship with the power of computing to assist researchers and practitioners for better understanding, exploration and navigation of this space of information has become a challenging, albeit rewarding, topic in social network analysis and data mining. The constantly growing scale and enriching genres of network data always demand higher levels of efficiency, robustness and generalizability where existing approaches with successes on small, homogeneous network data are likely to fall short. We introduce MultiAspectForensics, a handy tool to automatically detect and visualize novel sub graph patterns within a local community of nodes in a heterogenous network, such as a set of vertices that form a dense bipartite graph whose edges share exactly the same set of attributes. We apply the proposed method on three data sets from distinct application domains, present empirical results and discuss insights derived from these patterns discovered. Our algorithm, built on scalable tensor analysis procedures, captures spectral properties of network data and reveals informative signals for subsequent domain-specific study and investigation, such as suspicious port-scanning activities in the scenario of cyber-security monitoring.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {203–210},
numpages = {8},
keywords = {pattern mining, heterogeneous networks, tensor analysis},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.64,
author = {Forestier, Mathilde and Velcin, Julien and Zighed, Djamel},
title = {Extracting Social Networks to Understand Interaction},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.64},
doi = {10.1109/ASONAM.2011.64},
abstract = {Web forums are a huge data source. They allow people to interact with unknown individuals. Studying forums shows that the interaction is not obvious only through the structure but also through the content of the post. Taking into account this observation, we extract a social network with different kinds of relationships i.e. the structural relation, the name and the text quotations relation. We present here the promising results we obtain, and the difficulties we face while extracting the quotations in this kind of textual content. These results are obtained from real data (from two information websites) which make the validation difficult. So, we create a validation protocol composed of two steps and based on human raters. Finally, we will see the objective of this work which is understanding interactions in order to extract the social roles of individuals.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {213–219},
numpages = {7},
keywords = {Interaction, Social Network, Text Mining},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.119,
author = {Yan, Baoshi and Bajaj, Lokesh and Bhasin, Anmol},
title = {Entity Resolution Using Social Graphs for Business Applications},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.119},
doi = {10.1109/ASONAM.2011.119},
abstract = {Social network such as Linked In maintains profiles for its members in a semi-structured format. A lot of business applications like ad targeting and content recommendations rely on canonicalization of data elements like companies, titles and schools for enabling fine grained advertising or recommending candidates for job postings. In this paper we explore the issues around resolving company names for hundreds of millions of member positions to known company entities using the social graph. We proposed a machine learning approach leveraging three dimensional feature sets including the social graph, social behavior and various content and demographic features. The experiments showed that our approach achieved high precision at a reasonable coverage and is significantly superior to a baseline content based approach.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {220–227},
numpages = {8},
keywords = {social network, entity resolution, social graph},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.88,
author = {Farrugia, Michael and Hurley, Neil and Quigley, Aaron},
title = {SNAP: Towards a Validation of the Social Network Assembly Pipeline},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.88},
doi = {10.1109/ASONAM.2011.88},
abstract = {A key problem for social network analysis is the lack of ground-truth data upon which to validate an analysis. Consider for example community-finding algorithms. The ``communities'' identified by such algorithms are typically justified on the basis of their structural properties, rather than on their ability to recover communities which can be independently verified. A ground truth of actual community data isn't always available and at best only partial ground-truth community information is. However, this problem isn't unique to community-finding algorithms. In previous publications, we introduced an automated Social Network Assembly Pipeline we refer to as SNAP. This is intended for the large scale actor identification, tie interference and strength measurement of social networks from non-relational data sets. In this paper we describe a validation study of SNAP through an intensive user-study of a portion of the individuals in the network. Individuals are asked to validate the network relationships uncovered by SNAP and where misclassified relationships are found, the individuals are interviewed in order to determine the underlying cause of the misclassification. The findings provide feedback on the rules through which relationships are inferred. For instance, it becomes clear that an error in actor identification can result in a propagation of this error though the network relations leading to follow-on relationship misclassifications. Also, we observe how outliers lead to a propagation of error in the inferred network. The results help us validate and invalidate different hypotheses we have about SNAP and suggests domain specific rule-sets for SNAP.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {228–235},
numpages = {8},
keywords = {user study, validation, social networks, network assembly},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.116,
author = {Wang, Chi and Tang, Jie and Sun, Jimeng and Han, Jiawei},
title = {Dynamic Social Influence Analysis through Time-Dependent Factor Graphs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.116},
doi = {10.1109/ASONAM.2011.116},
abstract = {Social influence, the phenomenon that the actions of a user can induce her/his friends to behave in a similar way, plays a key role in many (online) social systems. For example, a company wants to market a new product through the effect of ``word of mouth'' in the social network. It wishes to find and convince a small number of influential users to adopt the product, and the goal is to trigger a large cascade of further adoptions. Fundamentally, we need to answer the following question: how to quantify the influence between two users in a large social network? To address this question, we propose a pair wise factor graph (PFG) model to model the social influence in social networks. An efficient algorithm is designed to learn the model and make inference. We further propose a dynamic factor graph (DFG) model to incorporate the time information. Experimental results on three different genres of data sets show that the proposed approaches can efficiently infer the dynamic social influence. The results are applied to the influence maximization problem, which aims to find a small subset of nodes (users) in a social network that could maximize the spread of influence. Experiments show that the proposed approach can facilitate the application.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {239–246},
numpages = {8},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.69,
author = {Brodka, Piotr and Saganowski, Stanislaw and Kazienko, Przemyslaw},
title = {Group Evolution Discovery in Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.69},
doi = {10.1109/ASONAM.2011.69},
abstract = {Group extraction and their evolution are among the topics which arouse the greatest interest in the domain of social network analysis. However, while the grouping methods in social networks are developed very dynamically, the methods of group evolution discovery and analysis are still 'uncharted territory' on the social network analysis map. Therefore the new method for the group evolution discovery called GED is proposed in this paper. Additionally, the results of the first experiments on the email based social network together with comparison with two other methods of group evolution discovery are presented.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {247–253},
numpages = {7},
keywords = {social network, social network analysis, group dynamics, GED, group evolution, groups in social networks, user position},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.74,
author = {Lee, Chung-Hong and Yang, Hsin-Chang and Chien, Tzan-Feng and Wen, Wei-Shiang},
title = {A Novel Approach for Event Detection by Mining Spatio-Temporal Information on Microblogs},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.74},
doi = {10.1109/ASONAM.2011.74},
abstract = {Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {254–259},
numpages = {6},
keywords = {text mining, temporal analysis, microblogging, spatial analysis, topic detection},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.55,
author = {Lichtenwalter, Ryan and Chawla, Nitesh V.},
title = {DisNet: A Framework for Distributed Graph Computation},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.55},
doi = {10.1109/ASONAM.2011.55},
abstract = {With the rise of network science as an exciting interdisciplinary research topic, efficient graph algorithms are in high demand. Problematically, many such algorithms measuring important properties of networks have asymptotic lower bounds that are quadratic, cubic, or higher in the number of vertices. For analysis of social networks, transportation networks, communication networks, and a host of others, computation is intractable. In these networks computation in serial fashion requires years or even decades. Fortunately, these same computational problems are often naturally parallel. We present here the design and implementation of a master-worker framework for easily computing such results in these circumstances. The user needs only to supply two small fragments of code describing the fundamental kernel of the computation. The framework automatically divides and distributes the workload and manages completion using an arbitrary number of heterogeneous computational resources. In practice, we have used thousands of machines and observed commensurate speedups. Writing only 31 lines of standard C++ code, we computed betweenness centrality on a network of 4.7M nodes in 25 hours.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {263–270},
numpages = {8},
keywords = {distributed computing, map-reduce, betweenness centrality, graph algorithms, parallel processing},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.78,
author = {Brocheler, Matthias and Pugliese, Andrea and Subrahmanian, V. S.},
title = {Probabilistic Subgraph Matching on Huge Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.78},
doi = {10.1109/ASONAM.2011.78},
abstract = {Users querying massive social networks or RDF databases are often not 100% certain about what they are looking for due to the complexity of the query or heterogeneity of the data. In this paper, we propose "probabilistic subgraph" (PS) queries over a graph/network database, which afford users great flexibility in specifying "approximately" what they are looking for. We formally define the probability that a substitution satisfies a PS-query with respect to a graph database. We then present the PMATCH algorithm to answer such queries and prove its correctness. Our experimental evaluation demonstrates that PMATCH is efficient and scales to massive social networks with over a billion edges.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {271–278},
numpages = {8},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.107,
author = {Davis, Darcy and Lichtenwalter, Ryan and Chawla, Nitesh V.},
title = {Multi-Relational Link Prediction in Heterogeneous Information Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.107},
doi = {10.1109/ASONAM.2011.107},
abstract = {Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional a priori link prediction. In accordance with previous research on homogeneous networks, we further demonstrate that a supervised approach to link prediction can enhance performance and is easily extended to the heterogeneous case. Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {281–288},
numpages = {8},
keywords = {classification, link prediction, heterogeneous information networks},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.28,
author = {Lang, Juan and Wu, S. Felix},
title = {Social Network User Lifetime},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.28},
doi = {10.1109/ASONAM.2011.28},
abstract = {Online Social Network (OSN) operators are interested in promoting usage among their users, and try a variety of strategies to encourage use. Some recruit celebrities to their site, some allow third parties to develop applications that run on their sites, and all have features intended to encourage use. As important as usage is, we are unaware of any studies into what influences users to be active and to remain online. This paper is the first work studying the lifetime of OSN users, examining the factors that influence lifetime in one OSN, Buzz net. The major contributions of this work are the study of active lifetime, the features and behaviors that encourage activity, and the comparison of active lifetime to passive lifetime.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {289–296},
numpages = {8},
keywords = {churn, lifetime, activity},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.110,
author = {Negre, Elsa and Missaoui, Rokia and Vaillancourt, Jean},
title = {Predicting a Social Network Structure Once a Node Is Deleted},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.110},
doi = {10.1109/ASONAM.2011.110},
abstract = {Social networks are dynamic structures in which entities and links appear and disappear for different reasons. Starting from the observation that each entity has a more or less important role within the network, the objective of this article is to propose a method which exploits the role played by nodes to predict the new structure of a social network once one entity disappears. The role of a node in the network is expressed in terms of the number of interactions it has with the rest of the network. Two roles are considered: the leader and the mediator with their corresponding measure: the degree centrality and the betweenness centrality.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {297–304},
numpages = {8},
keywords = {role, leader, mediator, prediction, social network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.40,
author = {Yuasa, Tomoyuki and Shirayama, Susumu},
title = {A New Analysis Method for Simulations Using Node Categorizations},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.40},
doi = {10.1109/ASONAM.2011.40},
abstract = {Most research concerning the influence of network structure on phenomena taking place on the network focus on relationships between global statistics of the network structure and characteristic properties of those phenomena, even though local structure has a significant effect on the dynamics of some phenomena. In the present paper, we propose a new analysis method for phenomena on networks based on a categorization of nodes. First, local statistics such as the average path length and the clustering coefficient for a node are calculated and assigned to the respective node. Then, the nodes are categorized using the self-organizing map (SOM) algorithm. Characteristic properties of the phenomena of interest are visualized for each category of nodes. The validity of our method is demonstrated using the results of two simulation models.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {305–312},
numpages = {8},
keywords = {Data Mining, Complex Network, Multi-Agent Simulation},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.30,
author = {Ko, Pei-Chun and Buskens, Vincent},
title = {Dynamics of Adolescent Friendships: The Interplay between Structure and Gender},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.30},
doi = {10.1109/ASONAM.2011.30},
abstract = {This study investigates the network characteristics of adolescent friendship networks and the interaction between network characteristics and gender. Two competing hypotheses for closure and openness are derived and tested. Adolescents might strive for network closure, because this facilitates trust and trustworthiness among their friends. However, openness can also be beneficial because it facilitates establishing multi-layered identities and finding novel ideas for school tasks. The hypothesis for interaction between structure and gender is derived from the argument that gender influences the criteria for seeking and making friends during adolescence. SIENA is used to estimate the effects of network and individual characteristics on friendship formation. The data consists of longitudinal friendship nominations of 410 Taiwanese adolescents. We find that adolescents have a tendency to establish friendships that increase network closure. This tendency is stronger for male than for female adolescents in single-gender classes. On the contrary, the tendency towards network closure is stronger for female than for male adolescents in mixed-gender classes.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {313–320},
numpages = {8},
keywords = {Social Networks, Gender, Dynamics of Friendship, Adolescence},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.20,
author = {Sridhar, Usha and Mandyam, Sridhar},
title = {Information Sources Driving Social Influences: A New Model for Belief Learning in Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.20},
doi = {10.1109/ASONAM.2011.20},
abstract = {Non-Bayesian models for learning in social networks, such as the DeGroot model, are focused on updating beliefs using social influence weights, and study the achievement of convergence of beliefs to a consensus. In this paper, we propose a new construct to capture the notion of agents using additional information sources, such as media, to obtain multiple affirmations of belief information through an information score. We use this new construct to create a feedback learning loop that allows agents to learn beliefs with social influences varying dynamically with the credibility of the information on such beliefs. We build a new social learning mechanism, without the constraining row-stochasticity assumptions on social influence, and show that with information sources driving social influences, the beliefs can converge, and not necessarily to a consensus, even with strong connectivity. In this process of learning agents with a similar alignment between exogenous information sources and beliefs 'group' together, and this reflects in the structure of social influences. The results of the new learning algorithm are demonstrated on a small, fully connected social network.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {321–327},
numpages = {7},
keywords = {Information Sources, Belief Learning, Soial Influence, Social Networks},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.37,
author = {Ho, Chien-Tung and Li, Cheng-Te and Lin, Shou-De},
title = {Modeling and Visualizing Information Propagation in a Micro-Blogging Platform},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.37},
doi = {10.1109/ASONAM.2011.37},
abstract = {Micro-blogging is a type of social networking service that has become ubiquitous in Web 2.0 era. Micro-blogs allows bloggers to exchange information, discuss ideas, and share experiences with friends or even strangers with similar interests. In this paper, we try to identify ways to measure how information is propagated in micro-blogs. More specifically, we consider the following issues. (1) How to quantify a person s capability to disseminate ideas via a micro-blog. (2) How to measure the extent of propagation of a concept in a micro-blog. (3) How to demonstrate and visualize information propagation in a micro-blog. We propose methods to effectively measure each user s ability to disseminate information via micro-blogs. The design of the measure considers three factors: (a) the number of people influenced, (b) the speed of propagation, and (c) the geographic distance of the propagation. We also provide an online demonstration micro-blog system that allows the users to explore the information propagation. The system shows the propagation paths and social graphs, influence scores, timelines, and geographical information among people for the user-given terms.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {328–335},
numpages = {8},
keywords = {information propagation, microblogging},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.32,
author = {Lappas, Georgios},
title = {From Web Mining to Social Multimedia Mining},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.32},
doi = {10.1109/ASONAM.2011.32},
abstract = {Web mining is a well established field with many applications. Over the last years we experience a vast and rapidly growing amount of multimedia content that becomes available online. Web 2.0 and online social networks have dramatically influenced the growing amount of multimedia content due to the fact that users become more active producers and distributors of such multimedia context. Web mining techniques and theory need to be expanded in order to include new emerging research trends from mining such heterogeneous new web data. This work conceptualizes and introduces the concept of social multimedia mining as a new emerging research area that combines web mining research, multimedia research and social media research.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {336–343},
numpages = {8},
keywords = {Web Mining, Social Media, Social Network Analysis, Multimedia},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.91,
author = {Hoang, Tuan-Anh and Lim, Ee-Peng and Achananuparp, Palakorn and Jiang, Jing and Teow, Loo-Nin},
title = {Modeling Socialness in Dynamic Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.91},
doi = {10.1109/ASONAM.2011.91},
abstract = {Socialness refers to the ability to elicit social interaction and social links among people. It is a concept often associated with individuals. Although there are tangible benefits in socialness, there is little research in its modeling. In this paper, we study socialness as a property that can be associated with items, beyond its traditional association with people. We aim to model an item's socialness as a quantitative measure based on the how popular the item is adopted by members of multiple communities. We propose two socialness models, namely Basic and Mutual Dependency, to compute item socialness based on different sets of principles. In developing the Mutual Dependency Model, we demonstrate that items' socialness can be related to the socialness of communities. Our model have been evaluated on a set of users and application items from a mobile social network. We also conducted experiments to study how socialness can be related to network effects such as homophily, social influence and friendship formation.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {344–350},
numpages = {7},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.59,
author = {Huang, Han-Chang and Kao, Hung-Yu},
title = {CAIS: Community Based Annotation Insight Search in a Folksonomy Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.59},
doi = {10.1109/ASONAM.2011.59},
abstract = {Folksonomy systems provide a way for users to share and organize bookmarks. The social relationship among users has become stronger with the rapid development of new technologies. Finding the leading objects has become an important topic. These research topics are always centered around finding the most popular pages or experts. In this paper, we propose a new notion of expertise, which we call user insight. User insight denotes the user s expertise in finding Web pages that are useful or have the potential to be popular pages before other users find them. To address the issue, we refer to three major types of Web pages, namely, isolated, well-known, and burgeoning. Burgeoning pages are exceptionally useful and attractive for users in a folksonomy system. In our paper, we build a time-based algorithm to estimate user insight. In addition, we discuss the social relationship within fan networks, and we propose a link-based algorithm called CAIS (Community-based Annotation Insight Search) to realize the reinforcement between users, communities and pages. Finally, we design several experiments to evaluate the performance of CAIS and compare it to other approaches. We prove that CAIS has a better performance for the user ranking of simulated data and real data from Delicious.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {353–360},
numpages = {8},
keywords = {folksonomy, user insight, burgeoning, social relationship},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.118,
author = {Yu, Xiao and Pan, Ang and Tang, Lu-An and Li, Zhenhui and Han, Jiawei},
title = {Geo-Friends Recommendation in GPS-Based Cyber-Physical Social Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.118},
doi = {10.1109/ASONAM.2011.118},
abstract = {The popularization of GPS-enabled mobile devices provides social network researchers a taste of cyber-physical social network in advance. Traditional link prediction methods are designed to find friends solely relying on social network information. With location and trajectory data available, we can generate more accurate and geographically related results, and help web-based social service users find more friends in the real world. Aiming to recommend geographically related friends in social network, a three-step statistical recommendation approach is proposed for GPS-enabled cyber-physical social network. By combining GPS information and social network structures, we build a pattern-based heterogeneous information network. Links inside this network reflect both people's geographical information, and their social relationships. Our approach estimates link relevance and finds promising geo-friends by employing a random walk process on the heterogeneous information network. Empirical studies from both synthetic datasets and real-life dataset demonstrate the power of merging GPS data and social graph structure, and suggest our method outperforms other methods for friends recommendation in GPS-based cyber-physical social network.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {361–368},
numpages = {8},
keywords = {friend recommendation, cyber-physical, social network, gps},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.27,
author = {Liu, Yu-Chin and Chueh, Ti-Lin and Cheng, Yun-Shan},
title = {A Hybrid Approach to Discover MEC Interview Data with the Hierarchical Value Map of Social Networking Sites as an Example},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.27},
doi = {10.1109/ASONAM.2011.27},
abstract = {As the booming of social network sites (SNSs), people adapt to communicate and share information via internet recently. According to great business opportunities emerging in SNSs, entrepreneurs strive to explore the potential needs inside users and then provide interesting feature functions on SNS platforms. The Means-End Chain (MECs) research method has been widely used to explore customers perceived values in selecting products. It is a good approach to help entrepreneurs finding the most appreciated product features. But however, while adopting MECs, researchers suffer the hassle of defining Attribute, Consequence and Value elements (ACV elements) from interview data. In addition, such context analyzing work heavily relies on researchers subjective opinions, so that the research conclusions might be difficult to replicate and the contributions are limited. Therefore, this paper aims to propose hybrid miming techniques to automatically discover Attribute, Consequence and Value elements which are the most essential components in MEC approach. A case on studying customers perceived values of social network cites is conducted by the proposed hybrid approach, and the experimental results show our method can discover the ACV elements effectively.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {373–376},
numpages = {4},
keywords = {Social Network Sites, Means-End Chain, Text Mining},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.63,
author = {Perez, Charles and Lemercier, Marc and Birregah, Babiga and Corpel, Alain},
title = {SPOT 1.0: Scoring Suspicious Profiles on Twitter},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.63},
doi = {10.1109/ASONAM.2011.63},
abstract = {Everyday more than fifty million messages are generated by about two hundred million profiles on Twitter. Some users attempt to exploit the success of this micro logging platform and its relative freedom to perform malicious actions that can lead to identity or data theft. This work aims to propose a framework to assess suspicious behavior on Twitter. We present a tool developed for Scoring Suspicious Profiles On Twitter (SPOT1.0) through a three-dimensional indicator that involves the degree of aggressiveness, the visibility and the level of danger.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {377–381},
numpages = {5},
keywords = {support vector machine, suspicious profiles, aggressiveness, visibility, Twitter, level of danger, social network analysis},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.43,
author = {Dong, Yuxiao and Ke, Qing and Wang, Bai and Wu, Bin},
title = {Link Prediction Based on Local Information},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.43},
doi = {10.1109/ASONAM.2011.43},
abstract = {Link prediction in complex networks is an important issue in graph mining. It aims at estimating the likelihood of the existence of links between nodes by the know network structure information. Currently, most link prediction algorithms based on local information consider only the individual characteristics of common neighbors. In this paper, first, we study the link prediction results as the change of the exponent on the degree of common neighbors, and find some regular pattern between different networks and different exponent. After that, we come up with a new algorithm exploiting the interactions between common neighbors, namely Individual Attraction Index. To reduce the time complexity, we design a simple edition, called Simple Individual Attraction Index. We compare nine well-known local information metrics on eight real networks. The result proves well the best overall performance of these two new algorithms.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {382–386},
numpages = {5},
keywords = {link prediction, Individual Attraction Index, socal network analysis, Simple Individual Attraction Index, the degree of common neighbors},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.90,
author = {Alsaleh, Slah and Nayak, Richi and Xu, Yue},
title = {Finding and Matching Communities in Social Networks Using Data Mining},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.90},
doi = {10.1109/ASONAM.2011.90},
abstract = {The rapid growth in the number of users using social networks and the information that a social network requires about their users make the traditional matching systems insufficiently adept at matching users within social networks. This paper introduces the use of clustering to form communities of users and, then, uses these communities to generate matches. Forming communities within a social network helps to reduce the number of users that the matching system needs to consider, and helps to overcome other problems from which social networks suffer, such as the absence of user activities' information about a new user. The proposed system has been evaluated on a dataset obtained from an online dating website. Empirical analysis shows that accuracy of the matching process is increased using the community information.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {389–393},
numpages = {5},
keywords = {Social network, online communities, recommender system},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.111,
author = {Yang, Yang and Sun, Yizhou and Pandit, Saurav and Chawla, Nitesh V. and Han, Jiawei},
title = {Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.111},
doi = {10.1109/ASONAM.2011.111},
abstract = {Community detection or cluster detection in networks is a well-studied, albeit hard, problem. Given the scale and complexity of modern day social networks, detecting ``reasonable'' communities is an even harder problem. Since the first use of k-means algorithm in 1960s, many community detection algorithms have been invented - most of which are developed with specific goals in mind and the idea of detecting ``meaningful'' communities varies widely from one algorithm to another. With the increasing number of community detection algorithms, there has been an advent of a number of evaluation measures and objective functions such as modularity and internal density. In this paper we divide methods of measurements in to two categories, according to whether they rely on ground-truth or not. Our work is aiming to answer whether these general used objective functions are well consistent with the real performance of community detection algorithms across a number of homogeneous and heterogeneous networks. Seven representative algorithms are compared under various performance metrics, and on various real world social networks.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {394–397},
numpages = {4},
keywords = {Objective functions, social networks, Measurements, Benchmark network, community detection},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.100,
author = {Lee, Ting-Lin and Tu, In-Chen},
title = {The Way of Joining Consortia Leads to a Good Performance: A Case of Taiwan Bike Industry},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.100},
doi = {10.1109/ASONAM.2011.100},
abstract = {This The article use visually social network analysis (SNA) to present networking activities of Taiwan s bike and components firms interacting in domestic market. The study uses data from questionnaires of sixty-five bike and component firms to explore the influence of organizational learning, relationship quality and network position on organizational performance. Results show that relationship quality positively affects organizational learning and organizational performance, and network position partially affects organizational performance. It is worth noted that a non-A-TEAM member (say IDEAL, coded GB_01) also had a higher in-degree and closeness centrality, and a better performance as well. Here is a key point to be re-thinking: is joining consortia the best way to lead a good performance__ __ The answer is not fully supported by the case of Taiwan s bike industry.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {398–402},
numpages = {5},
keywords = {organizational learning, social network, network position, organizational performance, relationship quality},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.16,
author = {Wu, ChienHsing and Kao, ShuChen and Chang, JiWei and Chen, KaiYing},
title = {A Comparison of Cognitive Styles on the Behavior of Obtaining Knowledge: Field Independence vs. Field Dependence},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.16},
doi = {10.1109/ASONAM.2011.16},
abstract = {This paper compares the cognitive style of field independence/dependence (FI, FD) on the behavior of obtaining knowledge from online learning system. The process of obtaining knowledge is considered with respect to the involvement of knowledge provider s and receiver s cognition characteristics. The experiment is conducted where two groups with FI for one and FD for another are arranged. Data analysis results indicate that (1) FI group spends more time on the designed KO than FD does. For non-designed KO, contrarily, the FD group has more time spent than FI group. (2) FI style is more likely to acquire knowledge from internal sources while FD prefers external where they can perform social interactions as the primary means to acquire knowledge. (3) FI group is likely monotonous to stick firmly with common or preferred passive knowledge resources, on the other hand, the learning process of FD group seems to be more varied, and often passing through in-between several different knowledge sources. Discussion and implications are also addressed.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {403–404},
numpages = {2},
keywords = {Cognitive style, Cognitive system, Field independence, Field dependence, Learning behavior},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.66,
author = {Chen, Lin and Nayak, Richi and Xu, Yue},
title = {A Recommendation Method for Online Dating Networks Based on Social Relations and Demographic Information},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.66},
doi = {10.1109/ASONAM.2011.66},
abstract = {A new relationship type of social networks - online dating - are gaining popularity. With a large member base, users of a dating network are overloaded with choices about their ideal partners. Recommendation methods can be utilized to overcome this problem. However, traditional recommendation methods do not work effectively for online dating networks where the dataset is sparse and large, and a two-way matching is required. This paper applies social networking concepts to solve the problem of developing a recommendation method for online dating networks. We propose a method by using clustering, SimRank and adapted SimRank algorithms to recommend matching candidates. Empirical results show that the proposed method can achieve nearly double the performance of the traditional collaborative filtering and common neighbor methods of recommendation.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {407–411},
numpages = {5},
keywords = {clustering, online dating, SimRank},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.29,
author = {Huang, Yin-Fu and Lin, San-Des},
title = {Applying Multidimensional Association Rule Mining to Feedback-Based Recommendation Systems},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.29},
doi = {10.1109/ASONAM.2011.29},
abstract = {The main characteristic of collaborative filtering is to provide personalized recommendations to a customer based on the customer profile, without considering content information about domain items. In this paper, we investigated to use a relevance feedback mechanism in the collaborative recommendation system. First, we used the Self-organizing Map (SOM) method to avoid suffering from the scalability and sparsity problem in the collaborative filtering. In addition, we adopted the Statistical Attribute Distance (SAD) method which uses the similarity in statistics of customers ratings to calculate customer correlations, instead of using the statistics of customers that rate for similar items. Then, the multi-tier granule mining algorithm was used to find association rules. Finally, with the relevance feedback mechanism and the association rules, the recommendations could be refined to provide customers more relevance information.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {412–417},
numpages = {6},
keywords = {information retrieval, relevance feedback, association rule mining, collaborative filtering, recommended systems},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.24,
author = {Wang, Kai-Yu and Thongpapanl, Narongsak and Wu, Hui-Ju and Ting, I-Hsien},
title = {Identifying Structural Heterogeneities between Online Social Networks for Effective Word-of-Mouth Marketing},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.24},
doi = {10.1109/ASONAM.2011.24},
abstract = {Social networks are extremely important for word-of-mouth (WOM) marketing. However, marketers often ignore network structures when developing WOM marketing strategies. Specifically, there is clearly a lack of research on looking into overall structures and structural heterogeneities of social networks. This research investigates structural heterogeneities between online social networks in different product categories. We collected data from four online networks in different product categories from the most popular social networking site in Taiwan. Social network analysis was performed to understand the network structures. The findings demonstrate the structural heterogeneities between these networks and we also provide managerial implications for practitioners.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {418–422},
numpages = {5},
keywords = {Social Network Analysis (SNA), Network Structure, Structural Heterogeneity, Word-of-Mouth (WOM) Marketing, Social Network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.70,
author = {Thomaidou, Stamatina and Vazirgiannis, Michalis},
title = {Multiword Keyword Recommendation System for Online Advertising},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.70},
doi = {10.1109/ASONAM.2011.70},
abstract = {As search engines, social networks, and the World Wide Web become more popular and widely used, online advertising turns into a very profitable industry. Individuals and companies promote their products or services in search engines through textual ads, alongside the organic search results triggered by a specific query. For this purpose, advertisers must create advertising campaigns. The development of these campaigns is a laborious task involving significant human resources and expertise. In this paper we propose a system for multiword keyword recommendations in the context of developing a web advertising campaign in a semiautomatic manner. Given a landing page, the system extracts relevant terms consisted of two or three words to match a potential search query. Furthermore, it proposes the most relevant keywords and other suggested terms that do not exist in the landing page text using search result snippets. In addition, we present blind testing experiments on real world data indicating that our approach outperforms prominent existing industrial solutions in most of the cases.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {423–427},
numpages = {5},
keywords = {online advertising, sponsored search, textual advertising, keyword selection},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.121,
author = {Chapsky, Daniel},
title = {Leveraging Online Social Networks and External Data Sources to Predict Personality},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.121},
doi = {10.1109/ASONAM.2011.121},
abstract = {Over the past decade, people have been expressing more and more of their personalities online. Online social networks such as Facebook.com capture much of individuals' personalities through their published interests, attributes and social interactions. Knowledge of an individual's personality can be of wide utility, either for social research, targeted marketing or a variety of other fields A key problem to predicting and utilizing personality information is the myriad of ways it is expressed across various people, locations and cultures. Similarly, a model predicting personality based on online data which cannot be extrapolated to "real world" situations is of limited utility for researchers. This paper presents initial work done on generating a probabilistic model of personality which uses representations of people's connections to other people, places, cultures, and ideas, as expressed through Face book. To this end, personality was predicted using a machine learning method known as a Bayesian Network. The model was trained using Face book data combined with external data sources to allow further inference. The results of this paper present one predictive model of personality that this project has produced. This model demonstrates the potential of this methodology in two ways: First, it is able to explain up to 56% of all variation in a personality trait from a sample of 615 individuals. Second it is able to clearly present how this variability is explained through findings such as how to determine how agreeable a man is based on his age, number of Face book wall posts, and his willingness to disclose his preference for music made by Lady Gaga.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {428–433},
numpages = {6},
keywords = {Personality, Facebook, Bayesian Network},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.21,
author = {Stan, Johann and Do, Viet-Hung and Maret, Pierre},
title = {Semantic User Interaction Profiles for Better People Recommendation},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.21},
doi = {10.1109/ASONAM.2011.21},
abstract = {In this paper we present a methodology for learning user profiles from content shared by people on Social Platforms. Such profiles are specifically tailored to reflect the user's degree of interactivity related to the topics they are writing about. The main novelty in our work is the introduction of Linked Data in the content extraction process and the definition of specific scores to measure expertise and interactivity.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {434–437},
numpages = {4},
keywords = {social network, user profile, linked data, analysis, recommendation, semantic},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.113,
author = {Shim, Kyong Jin and Hsu, Kuo-Wei and Srivastava, Jaideep},
title = {Modeling Player Performance in Massively Multiplayer Online Role-Playing Games: The Effects of Diversity in Mentoring Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.113},
doi = {10.1109/ASONAM.2011.113},
abstract = {This study investigates and reports preliminary findings on player performance prediction approaches which model player's past performance and social diversity in mentoring network in Ever Quest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. Our contributions include a better understanding of performance metrics used in the game and a foundation of recommendation systems for mentors and apprentices. We examined three different game servers from the Ever Quest II game logs. In all three servers, the results from our analyses suggest that increase in social diversity in terms of characters and classes encountered moderately negatively correlates with player performance. Based on this finding, we built predictive models to predict player's future performance based on past performance and social diversity in terms of mentoring activities. Our results indicate that 1) models employing past performance and social diversity perform better and 2) prediction for mentors is generally better than that for apprentices.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {438–442},
numpages = {5},
keywords = {video games, mentoring, player performance, massively multiplayer online games},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.105,
author = {Chester, Sean and Srivastava, Gautam},
title = {Social Network Privacy for Attribute Disclosure Attacks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.105},
doi = {10.1109/ASONAM.2011.105},
abstract = {Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected. To address this gap, we introduce an approach to anonymize social networks that have labeled nodes, $alpha$-proximity, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve $alpha$-proximity and experimentally validate the quality of the solutions it derives.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {445–449},
numpages = {5},
keywords = {algorithms, anonymization, social networks, attribute disclosure, data privacy},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.19,
author = {Cho, Kyung Soo and Yoon, Jae Yoel and Kim, Iee Joon and Lim, Ji Yeon and Kim, Seung Kwan and Kim, Ung-Mo},
title = {Mining Information of Anonymous User on a Social Network Service},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.19},
doi = {10.1109/ASONAM.2011.19},
abstract = {The growing number of individuals is recently writing their own opinions or information freely at the network space on the web such as the blog or Online Cafe and these network spaces are developed toward a new service called social network. Consequently, a lot of researchers are studying this social network lively. The social network is not only a tool for providing real time news, but also having an effect on an opinion or a policy decision. It uses mainly texts to present information in real-time, and its users use a computer or a mobile device to upload their own opinions or ideas. A lot of texts from users contain important and various information. We will get this information we need real-time if semantic can be extracted from texts on a social network. In this case, Opinion mining should be useful to extract semantic from social network. This paper suggests a noble method to grasp information of anonymous users through relationship information available and their psychology that is reflected on texts and also understand the meanings of contents in depth.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {450–453},
numpages = {4},
keywords = {LIWC, Relation number, Anonymous, Social network service, Opinion mining},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.84,
author = {Qureshi, M. Atif and Younus, Arjumand and Touheed, Nasir and Qureshi, M. Shahid and Saeed, Muhammad},
title = {Discovering Irrelevance in the Blogosphere through Blog Search},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.84},
doi = {10.1109/ASONAM.2011.84},
abstract = {Web 2.0 technologies have given birth to the blogosphere, which is an information sharing medium by the users for the users. Furthermore, these technologies have also expanded the search problem to a new form of search known as blog search. Similar to Web search, blog search has been affected by spam which affects the quality of search results. This paper approaches the relevant blog problem in the top search results against the general topic queries. It pursues a study of irrelevant blogs appearing in the top search results of Google Blog Search for the blog spot domains. We define metrics for irrelevant blogs by observing the qualitative relevance of content and by analyzing the link structure of those blogs. Our preliminary results show an overall recall of 0.875 with a precision of 1.0 for finding irrelevant blogs in the top 15 search results against six general topic queries on Google Blog Search.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {457–460},
numpages = {4},
keywords = {splogs, link structure, blogosphere, blog search, irrelevance, content-based},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.76,
author = {Nizamani, Sarwat and Memon, Nasrullah and Wiil, Uffe Kock and Karampelas, Panagiotis},
title = {CCM: A Text Classification Model by Clustering},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.76},
doi = {10.1109/ASONAM.2011.76},
abstract = {In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based approach to text classification tasks simplifies the model and at the same time increases the accuracy.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {461–467},
numpages = {7},
keywords = {NB, Clustering, ID3, Boosting, SVM, K-means, Classification},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.94,
author = {Ozgul, Fatih and Atzenbeck, Claus and Erdem, Zeki},
title = {How Much Similar Are Terrorists Networks of Istanbul?},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.94},
doi = {10.1109/ASONAM.2011.94},
abstract = {Most of terrorist groups cooperate, interchange knowledge, skills and materials used for attacks. Terrorist groups in Istanbul are categorized into three main groups within criminological viewpoint: extreme left (i.e. Marxist) groups, extreme right (i.e. Fundamentalist, Radical Islamist) groups, and separatist (i.e. ethnic, racist) groups. Crime ontology for terrorist groups in Istanbul is created by using their criminal history and choices such as selection of crimes, attacking methods and modus operandi. Terrorist groups of Istanbul are attached to this ontology as nodes connected to their attacks. A similarity measure (COSM) is developed according to this ontology. COSM results for Istanbul terrorist groups performed better than two common similarity measures, cosine and Jaccard. COSM similarity result is presented to domain experts in hierarchical clustering and they gave positive feedback. COSM, which is based on attributes of crimes, can also be applied to other types of social networks for measuring similarity.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {468–472},
numpages = {5},
keywords = {similarity, Terrorist networks, ontology},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.73,
author = {Ostoic, J. Antonio Rivero},
title = {Dyadic Patterns in Multiple Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.73},
doi = {10.1109/ASONAM.2011.73},
abstract = {Seven dyadic configuration classes for the analysis of multiple networks are introduced in this paper. Each configuration is a relational bundle pattern resulting from the mixture of the direction and the types of tie between a pair of actors. Then a bundle census is performed to an empirical network made of three kinds of relations, and the algorithm used for the bundle class detection is presented with a generalization of networks up to r types of tie.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {475–481},
numpages = {7},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.103,
author = {Berlingerio, Michele and Coscia, Michele and Giannotti, Fosca and Monreale, Anna and Pedreschi, Dino},
title = {Foundations of Multidimensional Network Analysis},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.103},
doi = {10.1109/ASONAM.2011.103},
abstract = {Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {485–489},
numpages = {5},
keywords = {social network, multidimensional networks, complex networks, multiple relations},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.104,
author = {Berlingerio, Michele and Coscia, Michele and Giannotti, Fosca},
title = {Finding and Characterizing Communities in Multidimensional Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.104},
doi = {10.1109/ASONAM.2011.104},
abstract = {Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem studied so far in complex network analysis is Community Discovery, i.e. the detection of group of nodes densely connected, or highly related. However, one aspect of such networks has been disregarded so far: real networks are often multidimensional, i.e. many connections may reside between any two nodes, either to reflect different kinds of relationships, or to connect nodes by different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensionality. In this paper, we attempt to do so, by defining the problem in the multidimensional context, and by introducing also a new measure able to characterize the communities found. We then provide a complete framework for finding and characterizing multidimensional communities. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {490–494},
numpages = {5},
keywords = {community discovery, complex networks, data mining, multidimensional networks},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.67,
author = {Brodka, Piotr and Stawiak, Pawel and Kazienko, Przemyslaw},
title = {Shortest Path Discovery in the Multi-Layered Social Network},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.67},
doi = {10.1109/ASONAM.2011.67},
abstract = {Multi-layered social networks consist of the fixed set of nodes linked by multiple connections. These connections may be derived from different types of user activities logged in the IT system. To calculate any structural measures for multi-layered networks this multitude of relations should be coped with in the parameterized way. Two separate algorithms for evaluation of shortest paths in the multi-layered social network are proposed in the paper. The first one is based on pre-processing--aggregation of multiple links into single multi-layered edges, whereas in the second approach, many edges are processed 'on the fly' in the middle of path discovery. Experimental studies carried out on the DBLP database are presented as well.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {497–501},
numpages = {5},
keywords = {multi-layered social network, shorthest path, social network analysis, social network, multi-layered edge},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.47,
author = {Yang, Bo and Liu, Jiming},
title = {Discovering Explorative Patterns from Real-World Complex Networks},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.47},
doi = {10.1109/ASONAM.2011.47},
abstract = {The ability to discover patterns of networks is fundamental for structural analysis applied to them. Many ubiquitous patterns demonstrated by real-world networks have been discovered, and corresponding tools for finding them also have been developed. Although existing works have greatly improved our understanding on networks, it is still challenging to precisely model and predict their behaviors mainly because their non-trivial structures usually consists of many fold coexisting patterns which cannot be appropriately and totally uncovered by a single tool exclusively designed for pre-defined ones. In this work, we take an effort to address this issue by introducing a parameter-free algorithm aiming to discover such patterns hidden in an explorative network.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {502–506},
numpages = {5},
keywords = {complex network, social network mining},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.11,
author = {Qin, Ting Ting and Fujita, Satoshi},
title = {Automatic Tag Attachment Scheme for Efficient File Search in Peer-to-Peer File Sharing Systems},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.11},
doi = {10.1109/ASONAM.2011.11},
abstract = {In this paper, we consider the problem of automatic tag attachment to the documents distributed over a P2P network aiming at improving the efficiency of file search in such networks. The proposed scheme combines text clustering with a modified tag extraction algorithm, and is executed in a fully distributed manner. We conducted experiments to evaluate the accuracy of the proposed scheme. The result of experiments indicates that for more than 90% of documents, it attaches the same tags as the ones attached by human reviewers.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {507–511},
numpages = {5},
keywords = {automatic tag attachment, TF-IDCF, text clustering, K-DMeans, P2P system},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.44,
author = {Rallis, Michalis and Vazirgiannis, Michalis},
title = {Rank Prediction in Graphs with Locally Weighted Polynomial Regression and EM of Polynomial Mixture Models},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.44},
doi = {10.1109/ASONAM.2011.44},
abstract = {In this paper we describe a learning framework enabling ranking predictions for graph nodes based solely on individual local historical data. The two learning algorithms capitalize on the multi feature vectors of nodes in graphs that evolve in time. In the first case we use weighted polynomial regression (LWPR) while in the second we consider the Expectation Maximization (EM) algorithm to fit a mixture of polynomial regression models. The first method uses separate weighted polynomial regression models for each web page, while the second algorithm capitalizes on group behavior, thus taking advantage of the possible interdependence between web pages. The prediction quality is quantified as the similarity between the predicted and the actual rankings and compared to alternative baseline predictor. We performed extensive experiments on a real world data set (the Wikipedia graph). The results are very encouraging.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {515–519},
numpages = {5},
keywords = {Mixture Models, Polynomial Regression, Maximum Likelihood Estimation, Locally Weighted Regression, Expectation-Maximization, Clustering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.57,
author = {Uddin, Shahadat and Hossain, Liaquat},
title = {Time Scale Degree Centrality: A Time-Variant Approach to Degree Centrality Measures},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.57},
doi = {10.1109/ASONAM.2011.57},
abstract = {In this paper, we introduce a time-variant approach to degree centrality measure - time scale degree centrality (TSDC), which considers both presence and duration of links among actors within a network, whereas, the traditional degree centrality approach regards only the presence or absence of links. We illustrate the difference between traditional and time scale degree centrality measure by applying these two approaches to explore the impact of 'degree' attributes of doctor-patient network that evolves during patient hospitalization period on the hospital length of stay (LOS) both in macro- and micro-level. In macro-level, both the traditional and time-scale approaches to degree centrality can explain the relationship between the 'degree' attribute of doctor-patient network and LOS. However, at micro-level or small cluster level, TSDC provides better explanation while traditional degree centrality approach is impotent to explain the relationship between them.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {520–524},
numpages = {5},
keywords = {time scale degree centrality, traditional degree centrality, centrality},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.22,
author = {Liang, Chen and Hiremagalore, Sharath and Stavrou, Angelos and Rangwala, Huzefa},
title = {Predicting Network Response Times Using Social Information},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.22},
doi = {10.1109/ASONAM.2011.22},
abstract = {Social networks and discussion boards have become a significant outlet where people communicate and express their opinion freely. Although the social networks themselves are usually well-provisioned, the participating users frequently point to external links to substantiate their discussions. Unfortunately, the sudden heavy traffic load imposed on the external, linked web sites causes them to become unresponsive leading to the \^{a} Flash Crowds\^{a} effect. In this paper, we quantify the prevalence of flash crowd events for a popular social discussion board (Digg). We measured the response times of 1289 unique popular websites. We were able to verify that 89% of the popular URLs suffered variations in their response times. By analyzing the content and structure of the social discussions, we were able to forecast accurately for 86% of the popular web sites within 5 minutes of their submission and 95% of the sites when more (5 hours) of social content became available. Our work indicates that we can effectively leverage social activity to forecast network events that will be otherwise infeasible to anticipate.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {527–531},
numpages = {5},
keywords = {Flash Crowds, Social Content Data Mining, Social Networks, Traffic Prediction, Website Response Time},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.89,
author = {Wang, Min-Feng and Jheng, Sie-Long and Tsai, Meng-Feng and Tang, Cheng-Hsien},
title = {Enterprise Email Classification Based on Social Network Features},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.89},
doi = {10.1109/ASONAM.2011.89},
abstract = {With the popularity of multimedia and network technologies, it is now often to attach large size of multimedia dataset to emails. However, delivering large volume of multimedia data over an enterprise email system can easily bring down the quality of overall network service. Moreover, without some sort of restrictions, many enterprises found that the network resource was occupied for personal interests. The business communication over emails thus suffers undesirable delays and cause damages to businesses. The competition to use email service therefore become an issue that many enterprises have to deal with. Obviously, enterprises should manage the email service so that business emails have the priority over personal usages. This management requires an effective methodology to classify enterprise emails into official and private emails, and the development of the method is the goal of this work. To achieve the accuracy of a desired classification methodology, we normally anticipated the developed method to survey as much information as possible. On the other hand, monitoring details of the email contents not only can decrease the performance of the method, but it also may violate the privacy rights that many legal regulation systems now protect. The balance of pursuing accurate classification and protecting privacy rights becomes a challenge for this problem. With the discussed challenges in mind, we develop an email classification method based on social features, rather than surveying the email contents. To the best of our knowledge, this paper is the first study to address the aforementioned problems. We obtain social features from emails to represent the input vector of support vector machine (SVM) classifier. Preliminary results show that our methodology can classify emails with a high accuracy. Compared with the other content-based feature of email, our work shows that exploring social features is a promising direction to solve similar email classification problems.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {532–536},
numpages = {5},
keywords = {Social Network, Enterprise Email Classification},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.95,
author = {Sert, Onur Can and Dursun, Kayhan and \"{O}zyer, Tansel},
title = {Ensemble of Multi-Objective Clustering Unified with H-Confidence Metric as Validity Metric},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.95},
doi = {10.1109/ASONAM.2011.95},
abstract = {Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall, ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical, further on mixed credit card dataset with different objectives, and compared them against single objective clustering result in terms of purity.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {537–541},
numpages = {5},
keywords = {NSGA-II, h-confidence, Multi-Objective Clustering},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.42,
author = {Cabunducan, Gerard and Castillo, Ralph and Lee, John Boaz},
title = {Voting Behavior Analysis in the Election of Wikipedia Admins},
year = {2011},
isbn = {9780769543758},
publisher = {IEEE Computer Society},
address = {USA},
url = {https://doi.org/10.1109/ASONAM.2011.42},
doi = {10.1109/ASONAM.2011.42},
abstract = {Past work analyzing elections in online domains has largely ignored the underlying social networks present in such environments. Here, we study the Wikipedia Request for Adminship (RfA) process within the context of a social network and pinpoint several factors influencing different stages of the voting process. The facets explored are: election participation, decision making in elections, and election outcome. We find that voters tend to participate in elections that their contacts have participated in. Furthermore, there is evidence showing that an individual's decision-making is influenced by his contacts' actions. The properties of voters within the social graph were also studied, results reveal that candidates who gain the support of an influential coalition tend to succeed in elections.},
booktitle = {Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining},
pages = {545–547},
numpages = {3},
keywords = {social network analysis, logistic regression, social voter, data mining},
series = {ASONAM '11}
}

@inproceedings{10.1109/ASONAM.2011.34,
author = {Wang, Jau-Hwang and Lin, Chien-Lung},
title = {An Association Model Based on Modus Operandi Mining for Implicit Crime Link Construction},
year = {2011},
isbn = {9780769

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr

branch_name: refs/heads/main