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Journal of the ACM (JACM)
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ACM Transactions on Information Systems (TOIS)
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UM '07 Proceedings of the 11th international conference on User Modeling
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On Discovering Community Trends in Social Networks
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Transient crowd discovery on the real-time social web
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Social communities connect people of similar interests together and play essential roles in social network applications. Examples of such communities include people who like the same objects on Facebook, follow common subjects on Twitter, or join similar groups on LinkedIn. Among communities, we notice that some of them are {\em magnetic} to people. A {\em magnet community} is such a community that attracts significantly more people's interests and attentions than other communities of similar topics. With the explosive number of self-formed communities in social networks, one important demand is to identify magnet communities for users. This can not only track attractive communities, but also help improve user experiences and increase their engagements, e.g., the login frequencies and user-generated-content qualities. In this paper, we initiate the study of magnet community identification problem. First we observe several properties of magnet communities, such as attention flow, attention qualify, and attention persistence. Second, we formalize these properties with the combination of community feature extraction into a graph ranking formulation based on constraint quadratic programming. In details, we treat communities of a network as super nodes, and their interactions as links among those super nodes. Therefore, a network of communities is defined. We extract community's magnet features from heterogeneous sources, i.e., a community's standalone features and its dependency features with other communities. A graph ranking model is formulated given these features. Furthermore, we define constraints reflecting communities' magnet properties to regularize the model. We demonstrate the effectiveness of our framework on real world social network data.