›› 2011, Vol. 26 ›› Issue (5): 778-791.doi: 10.1007/s11390-011-0177-0

• Special Section on Community Analysis and Information Recommendation • Previous Articles     Next Articles

Detecting Communities in K-Partite K-Uniform (Hyper)Networks

Xin Liu (刘欣) and Tsuyoshi Murata, Member, ACM, IEEE   

  1. Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, Japan
  • Received:2010-10-01 Revised:2011-07-04 Online:2011-09-05 Published:2011-09-05
  • Contact: Xin Liu E-mail:tsinllew@ai.cs.titech.ac.jp; murata@cs.titech.ac.jp
  • About author:Xin Liu is a Ph.D. candidate in the Department of Computer Science, Tokyo Institute of Technology. He received the B.S. degree in computing and information science from Wuhan University of Technology in 2004, and the M.S. degree in computer science from Wuhan University in 2007. His research interests includeWeb mining and social network analysis.
    Tsuyoshi Murata is an associate professor in the Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology. He obtained his doctor's degree in computer science at Tokyo Institute of Technology in 1997, on the topic of machine discovery of geometrical theorems. At Tokyo Institute of Technology, he conducts research on Web mining, artificial intelligence, and social network analysis. He is a member of IEEE, AAAI, ACM, JSAI, IPSJ and JSSST.
  • Supported by:

    This work was supported in part by JSPS Grant-in-Aid under Grant No. 22300049 and IBM Ph.D. Fellowship.

In social tagging systems such as Delicious and Flickr, users collaboratively manage tags to annotate resources. Naturally, a social tagging system can be modeled as a (user, tag, resource) hypernetwork, where there are three different types of nodes, namely users, resources and tags, and each hyperedge has three end nodes, connecting a user, a resource and a tag that the user employs to annotate the resource. Then how can we automatically cluster related users, resources and tags, respectively? This is a problem of community detection in a 3-partite, 3-uniform hypernetwork. More generally, given a K-partite K-uniform (hyper)network, where each (hyper)edge is a K-tuple composed of nodes of K different types, how can we automatically detect communities for nodes of different types? In this paper, by turning this problem into a problem of finding an efficient compression of the (hyper)network's structure, we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities, and develop a fast community detection method based on optimization. Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive, parameter-free, and scalable. We compare our method with existing methods in both synthetic and real-world datasets.

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