Enhanced Fuzzy Clustering Approach for Overlapping Community Detection via Structural Neighborhood Similarity

The existence of complex networks can be observed in various real-world contexts, such as social, biological, and/or neurological networks. A critical analytical challenge in such networks is community detection, which entails detecting groupings of nodes with dense internal connectivity. Numerous studies have been conducted on the subject of overlapping communities, wherein nodes may concurrently belong to multiple communities. In this paper, we propose an enhanced fuzzy clustering method for overlapping community detection based on neighborhood similarity. The core idea is to observe the community membership as a continuous feature; hence, nodes can belong to more than one community following different levels of affiliations. Our method consists of four stages: first, we find local structural features; then, we make a neighborhood similarity matrix based on common neighbors; next, we give initial fuzzy memberships using an Enhanced Fuzzy C-Means approach; and last, we improve memberships using a local optimization strategy. We evaluated our method on various real-world datasets of differing sizes and determined that it outperforms multiple state-of-the-art techniques, as indicated by overlapping modularity, F-score, and statistical significance assessments. The proposed method is a useful and scalable solution that is easier to understand and more accurate.