A community detection approach based on network representation learning for repository mining

In this paper, we propose a novel heterogeneous graph-based model for capturing and handling all the complex and strongly-correlated information of a software Developer Social Network (DSN) to support several analytic tasks. In particular, we challenge the problem of automatically discovering communities of software developers sharing interests for similar projects by relying on Social Network Analysis (SNA) findings. To overcome the huge graph-size issue, we leverage different graph embedding techniques. Eventually, we evaluate the proposed approach with respect to state-of-the-art approaches from an efficiency and an effectiveness point of view by carrying out an experiment involving the GitHub dataset. •Heterogeneous graph has been designed to model developers interaction in projects.•Community detection algorithm has been designed by using graph embedding techniques.•Graph embedding techniques have been used to extract information from DSN;•Effectiveness and efficiency evaluation have been made on real dataset (Github).