Role Assorted Community Discovery for Weighted Networks
This paper considers the difficulties in community discovery, and comes up with a community discovery algorithm on the basis of role assorted thoughts. Previous work indicates that a robust approach to community detection is the maximization of inner communication and the minimization of the in-out interaction. Here the authors show that this problem can be solved accords to the role assorted method which give distinguish labels to vertices in the same community. This method leads them to a number of possible algorithms for detecting community structures in both unweighted and weighted networks. The applicability and expandability of algorithms proposed are illustrated with application to a variety of computer-generated networks and real-world complex networks.