Topic model-based link community detection with adjustable range of overlapping

Authors:
Le Yu;Bin Wu;Bai Wang
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China;Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

Complex networks have attracted much research attentions. Community detection is an important problem in complex network which is useful in a variety of applications such as information propagation, link prediction, recommendations and marketing. In this paper, we focus on discovering overlapping community structure using link partition. We proposed a LDA-based link partition (LBLP) method which can find communities with adjustable range of overlapping. This method employs topic model to detect link partition, which can calculate the community belonging factor for each link. Based on the belonging factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of our solution on both real-world and synthesized networks. The experiment results demonstrate that the approach can find meaningful and relevant link community structure.