Evolutionary community detection in complex and dynamic networks

The discovery of communities in complex networks is a challenging problem with various applications in the real world. Classic examples of networks include transport networks, the immune system, human brain and social networks. Given a certain grouping of nodes into communities, a good measure is needed to evaluate the quality of the community structure based on the definition that a strong community has dense intra-connections and sparse outside community links. This paper investigates several fitness functions in an evolutionary approach to community detection in complex networks. Moreover, these fitness functions are used to study dynamic networks using an extended evolutionary algorithm designed to handle changes in the network structure. Computational experiments are performed for several real-world networks which have a known community structure and thus can be evaluated. The obtained results confirm the ability of the proposed method to efficiently detect communities for both static and dynamic complex networks.