Combined neighborhood tabu search for community detection in complex networks
RAIRO - Operations Research - Recherche Opérationnelle, Tome 50 (2016) no. 2, pp. 269-283.

Community is one prominent feature of complex networks. Community detection is one important research topic in the area of complex networks analysis. In this paper, we introduce a new heuristic algorithm for community detection using the popular modularity measure. The proposed algorithm, called CNTS for combined neighborhood tabu search (CNTS), relies on a joint use of vertex move and merge operators to improve the quality of visited solutions. A dedicated tabu mechanism provides the algorithm with additional capacities to effectively explore the search space. Experiments using a collection of 21 well-known benchmark instances show that the proposed algorithm competes favorably with state-of-the-art algorithms.

Reçu le :
Accepté le :
DOI : 10.1051/ro/2015046
Classification : 90-08, 90C27
Mots clés : Community detection, heuristics, tabu search, graph partitioning, clustering, combinatorial optimization
Gach, Olivier 1 ; Hao, Jin-Kao 1, 2

1 LERIA, University of Angers, 2 Bd Lavoisier, 49045 Angers cedex 01, France.
2 Institut Universitaire de France, Paris, France.
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Gach, Olivier; Hao, Jin-Kao. Combined neighborhood tabu search for community detection in complex networks. RAIRO - Operations Research - Recherche Opérationnelle, Tome 50 (2016) no. 2, pp. 269-283. doi : 10.1051/ro/2015046. http://www.numdam.org/articles/10.1051/ro/2015046/

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