Recursive Structure Similarity: A Novel Algorithm for Graph Clustering

Abstract

A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.