A posteriori approach for community detection

Authors:
Chuan Shi;Zhen-Yu Yan;Xin Pan;Ya-Nan Cai;Bin Wu
Affiliations:
School of Computer, Beijing University of Posts and Telecommunications, Beijing, China;Research Department, Fair Isaac Corporation (FICO), San Rafael, CA;School of Computer, Beijing University of Posts and Telecommunications, Beijing, China;School of Computer, Beijing University of Posts and Telecommunications, Beijing, China;School of Computer, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Year:
2011

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Abstract

Conventional community detection approaches in complex network are based on the optimization of a priori decision, i.e., a single quality function designed beforehand. This paper proposes a posteriori decision approach for community detection. The approach includes two phases: in the search phase, a special multi-objective evolutionary algorithm is designed to search for a set of tradeoff partitions that reveal the community structure at different scales in one run; in the decision phase, three model selection criteria and the Possibility Matrix method are proposed to aid decision makers to select the preferable solutions through differentiating the set of optimal solutions according to their qualities. The experimeats in five synthetic and real social networks illustrate that, in one run, our method is able to obtain many candidate solutions, which effectively avoids the resolution limit existing in priori decision approaches. In addition, our method can discover more authentic and comprehensive community structures than those priori decision approaches.