Discovering Communities in Multi-relational Networks

Abstract

Multi-relational networks (in short as MRNs) refer to such networks including one-typed nodes but associated with each other in poly-relations. MRNs are prevalent in the real world. For example, interactions in social networks include various kinds of information diffusion: email exchange, instant messaging services and so on. Community detection is a long-standing yet very difficult task in social network analysis, especially when meeting MRNs. This chapter gradually explores the research into discovering communities from MRNs. It begins by introducing the generalized modularity of the MRN, which paves the way for applying modularity optimization-based community detection methods on MRNs. However, the mainstream methods for discovering communities on MRNs are to integrate information from multiple dimensions. The existing integration methods fall into four categories: network integration, utility integration, feature integration, and partition integration. Learning or ranking the weight for each relation in MRN constitutes building blocks of network, utility and feature integrations. Thus, we turn our attention into several co-ranking frameworks on MRNs. We then discuss two different kinds of partition integration strategies, including the frequent pattern mining based method and the consensus clustering based method. Finally, for the purpose of conducting performance validation, we present several techniques for constructing the MRN based on both multivariate data and forum data.