Are evolutionary computation-based methods comparable to state-of-the-art non-evolutionary methods for community detection?

One important aspect of graphs representing complex sys- tems is community (or group) structure|assigning vertices to groups, which have dense intra-group connections and relatively sparse inter-group connections. Community de- tection is of great importance in various domains, where real-world complex systems are represented as graphs, since communities facilitate our understanding of the graph and thus of the underlying system. However, this is known to be a hard optimization problem. In this study we pursue the following question: Have Evo- lutionary Computation-Based Methods proven their worth for this complex domain, or is it currently better to rely on other state-of-the-art methods? While several works com- pare state-of-the-art methods for community detection (see [8] and [11] for recent surveys), we are unaware of other attempts to compare methods based on evolutionary com- putation to other methods. After describing some recent algorithms for this problem, and comparing them in various ways, we conclude that evo- lutionary computation-based method for community detec- tion have indeed developed to hold their own against other methods for several variants of this problem. However, they still need to be applied to more difficult problems and im- prove further to make them in par with other methods.