Similarity-based Local Community Detection for Bipartite Networks

Dongming Chen, Wei Zhao, Dongqi Wang, Xinyu Huang


Local community detection aims to obtain the local communities to which target node belongs, by employing only partial information of the network. As a commonly used network model, bipartite applies naturally when modeling relations between two different classes of objects. There are three problems to be solved in local community detection, such as initial core node selection, expansion approach and community boundary criteria. In this work, a similarity-based local community detection  algorithm for bipartite networks (SLCDB) is proposed, the algorithm can be used to detect local community structure by only using either types of nodes of a bipartite network. Experiment on real data proves that SLCDB algorithm’s output community structure can achieve a very high modularity which outperforms most existing local community detection methods for bipartite networks.

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