A label propagation-based method for community detection in directed signed social networks

Community detection is one of the most essential issues in social networks analysis field. Among the available categories of algorithms, the label propagation algorithm-based (LPA-based) methods, due to their proper time complexity, are of high concern. As all the social networks explicitly or implicitly include signed relationships, the attempt here is to suggest an LPA-based approach for community detection in the directed signed social networks. The direction of edges is not addressed in available LPA-based community detection methods for signed social networks. In this respect, 1) a weighting method is suggested in order to utilize the direction information that converts the network into an undirected weighted signed social network, 2) this weight is combined with a second weight obtained from the sign information of the edges, and 3) the LPA is extended, where the combined weights are applied in label propagation. Moreover, the directed signed modularity and the directed signed flow-based capacity measures are proposed. The findings of the run experiments indicate that the proposed method as to the directed signed modularity, directed signed flow-based capacity, and frustration measures on real-world and synthetic data sets, outperforms its counterparts.