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Community Detection based on Node Relationship Classification

Community detection is a salient task in network analysis to understand the intrinsic structure of networks. In this paper, we propose a novel community detection algorithm based on node relationship classification. The node relationship between two neighboring nodes is defined as whether they affiliate to the same community. A trained binary classifier is deployed to classify the node relationship, which considers both the local influence from the two nodes themselves and the global influence from the whole network. According to the classified node relationship, community structure can be detected naturally. The experimental results on both realworld and synthetic networks demonstrate that our algorithm has a better performance compared to other representative algorithms.