Community Detection of Directed Network for Software Ecosystems Based on a Two-Step Information Dissemination Model

Abstract

A software ecosystem is a complex system that allows developers to cooperate with each other. Community is a universal and important topological property of networks. Detecting the communities of the software ecosystem is of great significance for analyzing its structural characteristics, discovering its hidden patterns, and predicting its behavior. Traditional community detection algorithms of complex networks are mostly for undirected networks. For the social network, the direction of information dissemination between developers cannot be ignored. In addition, the existing algorithms of community detection usually only consider direct influence between individuals while neglecting indirect relationships. To solve these problems, this paper presents a community detection method based on a two-step information dissemination model for the software ecosystem. First, a two-step information dissemination model is established to calculate the information gain of nodes. Second, a ranking method of developers' comprehensive influence is given through their influence vectors and information gains. Finally, communities are detected by taking the influential nodes as the cluster centers and the probability of information dissemination as the clustering direction. The proposed method is applied to community detection of typical software ecosystems in GitHub. The experimental results show that our method has good performance in the identification of community structure.