A Novel Similarity Measurement for Community Structure Detection
How to identify community structure is a fundamental problem for analysis of complex network. In this paper we propose a novel similarity matrix of the nodes for this purpose, which combines the information of adjacency matrix and common-neighbors matrix. We compare it with diffusion kernel similarity and adjacency matrix using several algorithms which are widely used in detecting community structure, including the standard nonnegative matrix factorization, symmetric nonnegative matrix factorization and spectral clustering. The results performed on the synthetic benchmark networks show that the novel similarity matrix is relatively effective to find the community structures in networks with heterogeneous distribution of node degrees and community sizes, and this effectiveness is also manifested on the real world networks.
similarity measure community structure detection nonnegative matrix factorization spectral clustering.
Junyong Jiao Di Hu Zhong-Yuan Zhang
School of Statistics, Central University of Finance and Economics Beijing, P.R.China
国际会议
南昌
英文
301-306
2012-08-26（万方平台首次上网日期，不代表论文的发表时间）