Complex network community detection by local similarity

Abstract

Complex networks are a typical form of representation of complex systems. Community structure is one of the most important structural characteristics of complex networks. In this paper, we propose a new measurement of similarity based on local structures for the purpose of detecting communities in complex networks. Compared to the similarity measures based on the entire network, the proposed similarity measure requires less computation and produces good descriptions of the structural characteristics of the networks. Meanwhile, it reverses the tendency of under-estimating produced by some existing similarity measures based on local structures. To utilize our measurement of similarity to the detection of community structures, we also generalize the Ward hierarchical clustering algorithm so that it is applicable to any object that has similarity measurement. And as an application we particularly employ this algorithm to detect community structures in complex networks. The proposed method is tested on both computer-generated and real-world networks, and is compared with the typical algorithms in community detection. Experimental results verify and confirm the feasibility and validity of the proposed method. Copyright © 2011 Acta Automatica Sinica. All rights reserved.