A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

DOI Web Site 38 References
  • XIN LIU
    Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan
  • WEICHU LIU
    Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan
  • TSUYOSHI MURATA
    Department of Computer Science, Tokyo Institute of Technology, 2-12-1, Ookayama, Meguro, Tokyo 152-8852, Japan
  • KEN WAKITA
    Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8552, Japan

Abstract

<jats:p> There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities. </jats:p>

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