A Local Community Detection Method Based on Folded Subgraph

Community structure refers to the “small groups” in the network. Detecting community structure in networks has significant application value. With the continuous expansion and complexity of the network, the global information of the network is often difficult to obtain. On the other hand, in some cases, we pay more attention to the local community where the given node is located. Local community detection methods detect local community structure by using local information from a given node. However, many local community detection methods encounter the problem of precision limitation. Therefore, in order to alleviate such problems, we propose the FG-based method in this paper. Based on the characteristics of complex networks, a folded subgraph method is designed to consider some similar nodes as single nodes, reducing the impact of noise in the network. Furthermore, based on the folded subgraph, FG-based method designs a three-stage local expansion strategy, in which nodes with different characteristics are added to the local community in each stage. We conduct experiments on datasets and find that the FG-based method can improve the recall and precision of local community structures.