Modularity maximization in networks by variable neighborhood search Finding communities or clusters, in networks, or graphs, has been the subject of intense studies in the last ten years. The most used criterion for that purpose, despite some recent criterions, is modularity maximization, proposed by Newman and Girvan. It consists in maximizing the sum for all clusters of the number of inner edges minus the expected number of inner edges assuming the same distribution of degrees. Numerous heuristics as well as a few exact algorithms have been proposed to maximize modularity. We apply the variable nighborhood search metaheuristic to that problem. Computational results are reported for the instances of the 10th DIMACS implementation challenge. The algorithm presented in this paper obtained the second prize in the quality modularity (sub-)challenge of the referred competitions, finding the best known solutions for 11 out of 30 instances.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Miasnikof, Pierre; Pitsoulis, Leonidas; Bonner, Anthony J.; Lawryshyn, Yuri; Pardalos, Panos M.: Graph clustering via intra-cluster density maximization (2020)
- Džamić, Dušan; Aloise, Daniel; Mladenović, Nenad: Ascent-descent variable neighborhood decomposition search for community detection by modularity maximization (2019)
- Biedermann, Sonja; Henzinger, Monika; Schulz, Christian; Schuster, Bernhard: Memetic graph clustering (2018)
- Santiago, Rafael de; Lamb, Luís C.: Exact computational solution of modularity density maximization by effective column generation (2017)
- Santiago, Rafael; Lamb, Luís C.: Efficient modularity density heuristics for large graphs (2017)
- Aloise, Daniel; Caporossi, Gilles; Hansen, Pierre; Liberti, Leo; Perron, Sylvain; Ruiz, Manuel: Modularity maximization in networks by variable neighborhood search (2013)