Community detection algorithm based on structural similarity for bipartite networks
Community detection has realistic meaning to the research of network structure. In this paper, we propose a community detection algorithm (BSSCD algorithm) by calculating the similarity of nodes and dividing the nodes with maximum similarity into the same community. In order to accurately calculate the similarity of nodes, we define a novel similarity calculation method which combines Salton index and the improved Logistic function by analyzing the structural characteristics of two types of nodes in bipartite networks and their effects on the density of community. BSSCD algorithm does not require prior knowledge about the number of communities and the result obtained with BSSCD algorithm is very stable. Experiments on real world network datasets show that the similarity calculation method can improve accuracy of similarity calculation and BSSCD algorithm is an efficient method for community detection in bipartite networks.
2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)