A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

Xin Liu (), Weichu Liu (), Tsuyoshi Murata () and Ken Wakita ()
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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;

Advances in Complex Systems (ACS), 2014, vol. 17, issue 06, 1-21

Abstract: 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.

Keywords: Community detection; community structure; modularity; heterogeneous multi-relational network; social network (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219525914500180

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