Improved Multilevel Algorithm to Detect Communities in Flight Networks

Abstract

Community detection in networks is a hard to solve combinatorial optimization problem with large-scale applications such as those based on flight networks. To approach this problem, a number of heuristics can be found in the literature, among them we highlight multilevel algorithms which are very efficient in detecting communities in larger networks. In this paper, we propose an improvement of a multilevel community detection algorithm that aims at finding communities so as to maximize the well-known modularity measure. The proposed improvement is the inclusion of a refinement phase to enhance the modularity value in flight networks we compiled. We presented a study case on the communities found for real flight networks. The vertices of the constructed networks correspond to airports in Europe, South America, United States of America and Canada and the arcs represent the flights whose weights indicate the number of flights between pairs of destinations. Experiments with these networks pointed to an improvement in the modularity of the communities found by the proposed algorithm when contrasted with its original version and the Lovain method. We visually identified consistent communities in the flight networks. Besides, in comparison to other reference algorithms on benchmark networks, ours was very competitive.