Evaluating Community Detection Using a Bi-objective Optimization

Community detection consists on a partitioning networks technique into clusters (communities) with weak coupling (external connectivity) and high cohesion (internal connectivity). In order to measure the performance of the clustering, the network modularity is largely used, a metric that presents the cohesion and the coupling of communities. In this paper, a global and bi-objective function is proposed to evaluate community detection. This function combines modularity (based on structure and edges weights) and the inter-classes inertia (based on nodes weights). Then, we rely on a computational optimization technique i.e. Particle Swarm Optimization to maximize this bi-objective quality. Finally, a case study evaluates the proposed solution and illustrates practical uses.