Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach

• URL: http://arxiv.org/abs/2004.02842v1
• Date: Mon, 6 Apr 2020 17:36:24 GMT
• Title: Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach
• Authors: Maoying Qiao, Jun Yu, Wei Bian, Dacheng Tao
• Abstract summary: Community is a common characteristic of networks including social networks, biological networks, computer and information networks. We propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks.
• Score: 89.19237792558687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Community is a common characteristic of networks including social networks, biological networks, computer and information networks, to name a few. Community detection is a basic step for exploring and analysing these network data. Typically, homogenous network is a type of networks which consists of only one type of objects with one type of links connecting them. There has been a large body of developments in models and algorithms to detect communities over it. However, real-world networks naturally exhibit heterogeneous qualities appearing as multiple types of objects with multi-relational links connecting them. Those heterogeneous information could facilitate the community detection for its constituent homogeneous networks, but has not been fully explored. In this paper, we exploit heterogeneous multi-relational networks (HMRNet) and propose an efficient message passing based algorithm to simultaneously detect communities for all homogeneous networks. Specifically, an HMRNet is reorganized into a hierarchical structure with homogeneous networks as its layers and heterogeneous links connecting them. To detect communities in such an HMRNet, the problem is formulated as a maximum a posterior (MAP) over a factor graph. Finally a message passing based algorithm is derived to find a best solution of the MAP problem. Evaluation on both synthetic and real-world networks confirms the effectiveness of the proposed method.

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