DECoDe - Differential Evolution Algorithm for Community Detection

Community structure of networks, i.e., groups of nodes densely connected inside the same group and weakly connected outside the group, are one of their most important property and there is great interest in the investigation of methods that are able to automatically detect such divisions. This work presents a novel method for community detection based on Differential Evolution, the Differential Evolution Algorithm for Community Detection (DECoDe). Differential evolution is an evolutionary algorithm frequently applied to continuous problems and this work presents a novel approach which adapts the algorithm to discrete problems, allowing the solution of the community detection problem. Several tests were executed with real networks and the results show that the presented approach is able to find consistent community structures, when compared to other methods in the literature.