  This paper studies community formation in OSS collaboration networks. While
most current work examines the emergence of small-scale OSS projects, our
approach draws on a large-scale historical dataset of 1.8 million GitHub users
and their repository contributions. OSS collaborations are characterized by
small groups of users that work closely together, leading to the presence of
communities defined by short cycles in the underlying network structure. To
understand the impact of this phenomenon, we apply a pre-processing step that
accounts for the cyclic network structure by using Renewal-Nonbacktracking
Random Walks (RNBRW) and the strength of pairwise collaborations before
implementing the Louvain method to identify communities within the network.
Equipping Louvain with RNBRW and the contribution strength provides a more
assertive approach for detecting small-scale teams and reveals nontrivial
differences in community detection such as users tendencies toward preferential
attachment to more established collaboration communities. Using this method, we
also identify key factors that affect community formation, including the effect
of users location and primary programming language, which was determined using
a comparative method of contribution activities. Overall, this paper offers
several promising methodological insights for both open-source software experts
and network scholars interested in studying team formation.
