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

Abstract

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.