Semi-supervised clustering algorithm for community structure detection in complex networks

Xiaoke Ma, Lin Gao, Xuerong Yong and Lidong Fu

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 1, 187-197

Abstract: Discovering a community structure is fundamental for uncovering the links between structure and function in complex networks. In this paper, we discuss an equivalence of the objective functions of the symmetric nonnegative matrix factorization (SNMF) and the maximum optimization of modularity density. Based on this equivalence, we develop a new algorithm, named the so-called SNMF-SS, by combining SNMF and a semi-supervised clustering approach. Previous NMF-based algorithms often suffer from the restriction of measuring network topology from only one perspective, but our algorithm uses a semi-supervised mechanism to get rid of the restriction. The algorithm is illustrated and compared with spectral clustering and NMF by using artificial examples and other classic real world networks. Experimental results show the significance of the proposed approach, particularly, in the cases when community structure is obscure.

Keywords: Semi-supervised clustering; Complex networks; Community structure; Nonnegative matrix factorization (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (21)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:1:p:187-197

DOI: 10.1016/j.physa.2009.09.018

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