Evolutionary community detection in social networks

As people that share common characteristics and interests tend to communicate with each other more frequently, they form communities within social networks. Several methods have been developed to discover such communities based on topological metrics. These methods have been used to successfully discover communities that are relatively large, but for communities characterized by members interacting more frequently with each other rather than interacting with many others, we propose here an effective method which is based on the use of an evolutionary algorithm (EA) called ECDA. Given a social network represented as a graph, unlike existing approaches, ECDA considers both topological metrics of the graph and the attributes of the vertices and edges when detecting for communities in the network. It performs its task by formulating the community detection problem as an optimization problem. By computing a measure of statistical significance for each attribute of the vertices, ECDA looks for communities in a network that have maximal connection significance within a community and minimal significance between any two communities. With such a strategy, ECDA partitions a network into different communities consisting of members with similar attributes within and different attributes without. Unlike other EAs, ECDA adopts a reproduction process consisting of special crossover and mutation operators, called Self-Evolution, to speed up the evolutionary process. ECDA has been tested with several real datasets and its performance is found to be very promising.