On selection of objective functions in multi-objective community detection

Authors:
Chuan Shi;Philip S. Yu;Yanan Cai;Zhenyu Yan;Bin Wu
Affiliations:
Beijing University of Posts and Telecommunications, Beijing, China;University of Illinois at Chicago, Chicago, USA;Beijing University of Posts and Telecommunications, Beijing, China;Fair Isaac Corporation, San Francisco, USA;Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

There is a surge of community detection of complex networks in recent years. Different from conventional single-objective community detection, this paper formulates community detection as a multi-objective optimization problem and proposes a general algorithm NSGA-Net based on evolutionary multi-objective optimization. Interested in the effect of optimization objectives on the performance of the multi-objective community detection, we further study the correlations (i.e., positively correlated, independent, or negatively correlated) of 11 objective functions that have been used or can potentially be used for community detection. Our experiments show that NSGA-Net optimizing over a pair of negatively correlated objectives usually performs better than the single-objective algorithm optimizing over either of the original objectives, and even better than other well-established community detection approaches.