2012 MagnetCommunityIdentificationon

• (Wang et al., 2012) ⇒ Guan Wang, Yuchen Zhao, Xiaoxiao Shi, and Philip S. Yu. (2012). “Magnet Community Identification on Social Networks.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339627

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• http://scholar.google.com/scholar?q=%222012%22+Magnet+Community+Identification+on+Social+Networks
• http://dl.acm.org/citation.cfm?id=2339530.2339627&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; magnet community; social networks

Abstract

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 magnetic to people. A [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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MagnetCommunityIdentificationon	Philip S. Yu Xiaoxiao Shi Guan Wang Yuchen Zhao			Magnet Community Identification on Social Networks				10.1145/2339530.2339627		2012