NSLS: A Neighbor Similarity and Label Selection-Based Algorithm for Community Detection

Community detection is still regarded as one of the most applicable methods for discovering latent information in complex networks. Recently, many similarity-based community detection algorithms have been widely applied to the analysis of complex networks. However, these approaches may also have some limitations, such as relying solely on simple similarity measures, which makes it difficult to differentiate the tightness of the relation between nodes. Aiming at this issue, this paper proposes a community detection algorithm based on neighbor similarity and label selection (NSLS). Initially, the algorithm assigns labels to each node using a new local similarity measure, thereby quickly forming a preliminary community structure. Subsequently, a similarity parameter is introduced to calculate the similarity between nodes and communities, and the nodes are reassigned to more appropriate communities. Finally, dense communities are obtained by a fast-merge method. Experiments on real-world networks show that the proposed method is accurate, compared with recent and classical community detection algorithms.