Multi-objective Decisionmaking in the Detection of Comprehensive Community Structures


Community detection in complex networks has attracted a lot of attentions in recent years. Compared with the traditional single-objective community detection approaches, the multi-objective approaches based on evolutionary computation can provide a decision maker with more flexible and promising solutions. How to make effective use of the optimal solution set returned by the multi-objective community detection approaches is an important yet unsolved issue. Through leveraging an existing multi-objective community detection algorithm, this paper proposes four model selection methods to aid the decision makers to select the preferable community structures. The experiments with three synthetic and real social networks illustrate that the proposed method can discover more authentic and comprehensive community structures than traditional single-objective approaches.