Two-Phase Genetic Algorithm for Social Network Graphs Clustering

An important and useful task of a social network analysis is partitioning of its users into clusters. The structure of a social network can be naturally modeled by a directed graph. This approach transforms clustering of the users into searching for highly connected sub graphs in such a social network model. Many different approaches and algorithms for this problem exist, one of the possibilities is to utilize genetic algorithms for solving this type of task. In this paper, we analyze several different genetic operators and propose evolutionary based algorithm for clustering in the domain of directed weighted graphs.