Separated and overlapping community detection in complex networks using multiobjective Evolutionary Algorithms

Both separated and overlapping communities are useful to analyze real networks in different situations. However, to the best of our knowledge, existing community detection methods based on Evolutionary Algorithms (EAs) can detect separate communities only. This is because it is difficult to represent overlapping communities in ways that are suitable for EAs. In this paper, we first design a representation method that can represent each individual as both separated and overlapping communities without assigning the number of communities in advance. We then design three objective functions to guide the evolutionary process in different conditions. Finally, based on the designed representation and objective functions, we propose a multiobjective evolutionary algorithm to solve CDPs (MEA_CDPs) under the framework of NSGA-II. In the experiments, 4 well-known real-life benchmark networks are used to validate the performance of MEA_CDPs, and the results shown that MEA_CDPs not only can find high quality communities, but also can detect both separated and overlapping communities at the same time, and present multiple types of communities. Moreover, the overlapping nodes identified by MEA_CDPs are really ambiguous according to their edge distributes in different communities. This illustrates the effectiveness of the objective functions we designed.