A Local Expansion and Community Merging Based Algorithm for Community Detection in Complex Networks

Community detection is an important issue in studying network structure and network characteristics. It has received widespread attention in many fields. Most existing community detection algorithms obtain the final community structure by analyzing the relationship between each node and surrounding nodes. Starting from a portion of nodes in each community, the corresponding community for each node can be obtained through expansion operations, thereby obtaining the entire community structure. Such strategy can improve the accuracy of community detection algorithms. In this study, we propose a local expansion and community merging (LECM) based algorithm. The LECM consists of four main parts. Firstly it chooses important nodes. In the second step, based on local modularity, important nodes are locally expanded to obtain local communities. Next, the merging degree standard is used to determine and merge local communities. Finally, the boundary overlapping nodes are reprocessed to ensure the accuracy of the community partition results. Several tests on five synthetic and two real-world networks are set to verify the effectiveness of the algorithm, the experimental result shows that LECM has better performance than two existing algorithms.