Detecting local communities in complex network via the optimization of interaction relationship between node and community

Abstract

The goal of local community detection algorithms is to explore the optimal community with a reference to a given node. Such algorithms typically include two primary processes: seed selection and community expansion. This study develops and tests a novel local community detection algorithm called OIRLCD that is based on the optimization of interaction relationships between nodes and the community. First, we introduce an improved seed selection method to solve the seed deviation problem. Second, this study uses a series of similarity indices to measure the interaction relationship between nodes and community. Third, this study uses a series of algorithms based on different similarity indices, and designs experiments to reveal the role of the similarity index in algorithms based on relationship optimization. The proposed algorithm was compared with five existing local community algorithms in both real-world networks and artificial networks. Experimental results show that the optimization of interaction relationship algorithms based on node similarity can detect communities accurately and efficiently. In addition, a good similarity index can highlight the advantages of the proposed algorithm based on interaction optimization.

Keywords

• Complex networks
• Local community detection
• Interaction relationship between nodes and community
• Node similarity index
• Local centrality