Collaborative similarity analysis of multilayer developer-project bipartite network

Abstract

To understand the multiple relations between developers and projects on GitHub as a whole, we model them as a multilayer bipartite network and analyze the degree distributions, the nearest neighbors' degree distributions and their correlations with degree, and the collaborative similarity distributions and their correlations with degree. Our results show that all degree distributions have a power-law form, especially, the degree distribution of projects in watching layer has double power-law form. Negative correlations between nearest neighbors' degree and degree for both developers and projects are observed in both layers, exhibiting a disassortative mixing pattern. The collaborative similarity of both developers and projects negatively correlates with degree in watching layer, while a positive correlations is observed for developers in forking layer and no obvious correlation is observed for projects in forking layer.