A Multiagent Evolutionary Method for Detecting Communities in Complex Networks

Abstract

Community structure detection in complex networks contributes greatly to the understanding of complex mechanisms in many fields. In this article, we propose a multiagent evolutionary method for discovering communities in a complex network. The focus of the method lies in the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the community detection problem. First, the method uses a connection-based encoding scheme to model an agent and a random-walk behavior to construct a solution. Next, it applies three evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents and solution evolution. We tested the performance of our method using synthetic and real-world networks. The results show its capability in effectively detecting community structures.

Multiobjective approach for detecting communities in heterogeneous networks

“…Therefore, our approach also seems to be useful for learning communities in social media analytics. A new line for further research in this area is the development of new evolutionary algorithms for detecting communities in a heterogeneous network by improving some successful algorithms in a homogeneous environment (2 examples of these algorithms are proposed by Cai et al and Ji et al). Another characteristic of our multiobjective approach is its ability to detect the communities in multimode networks. To do so, if we consider a separate SF for each mode, we can develop an algorithm that works similar to biclustering methods.…”