Clustering complex networks and biological networks by nonnegative matrix factorization with various similarity measures

Authors:
Rui-Sheng Wang;Shihua Zhang;Yong Wang;Xiang-Sun Zhang;Luonan Chen
Affiliations:
School of Information, Renmin University of China, Beijing 100872, China and Osaka Sangyo University, Osaka 574-8530, Japan;Academy of Mathematics and Systems Science, CAS, Beijing 100190, China and Graduate University of Chinese Academy of Sciences, Beijing 100049, China;Academy of Mathematics and Systems Science, CAS, Beijing 100190, China;Academy of Mathematics and Systems Science, CAS, Beijing 100190, China;Institute of Systems Biology, Shanghai University, Shanghai 200444, China and Osaka Sangyo University, Osaka 574-8530, Japan and ERATO Aihara Complexity Modelling Project, JST, Tokyo 151-0064, Jap ...
Venue:
Neurocomputing
Year:
2008

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Abstract

Identifying community structure in complex networks is closely related to clustering of data in other areas without an underlying network structure. In this paper, we propose a nonnegative matrix factorization (NMF)-based method for finding community structure. We first evaluate several similarity measures, such as diffusion kernel similarity, shortest path based similarity on several widely well-studied networks. Then, we apply NMF with diffusion kernel similarity to a large biological network, which demonstrates that our method can find biologically meaningful functional modules. Comparison with other algorithms also indicates the good performance of our method.