Title: 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.