A FRAMEWORK FOR COMMUNITY DETECTION IN HETEROGENEOUS MULTI-RELATIONAL NETWORKS

作者：Xin Liu, Weichu Liu, Tsuyoshi Murata, Ken Wakita · 发表于：Advances in Complex Systems · 年份：2014 · DOI：10.1142/s0219525914500180 · 被引用次数：35 · 研究领域：Complex Network Analysis Techniques、Caching and Content Delivery、Network Security and Intrusion Detection

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.