Abstract. 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 criticism, 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 Neighborhood Search metaheuristic to that problem. Computational results are reported for the instances of the 10 th DIMACS Implementation Challenge. The algorithm presented in this paper obtained the second prize in the quality modularity (sub)challenge of the referred competition, finding the best known solutions for 11 out of 30 instances.
VNS REF applies the Variable Neighborhood Search heuristic to the graph clustering problem.
Modularity maximization in networks by variable neighborhood search
Graph Partitioning and Graph Clustering
Computer Science