🤖 AI Summary
This study quantifies the impact of non-code contributions—such as network position, temporal activity patterns, and code review behavior—on contributor influence within open-source ecosystems. Leveraging 25 years of project data from the Cloud Native Computing Foundation, the work presents the first systematic integration of graph neural networks, temporal network analysis, and multidimensional contribution metrics. The authors develop GPU-accelerated implementations of PageRank and betweenness centrality, alongside a custom LSTM model, to identify five distinct contributor roles. Findings reveal that the top 1% of contributors disproportionately shape structural influence, with “Bridge”-type roles proving critical to network connectivity. The research further demonstrates that network metrics evolve significantly around project milestones and proposes a role-based framework for assessing community health.
📝 Abstract
Open source software (OSS) projects rely on complex networks of contributors whose interactions drive innovation and sustainability. This study presents a comprehensive analysis of OSS contributor networks using advanced graph neural networks and temporal network analysis on data spanning 25 years from the Cloud Native Computing Foundation ecosystem, encompassing sandbox, incubating, and graduated projects. Our analysis of thousands of contributors across hundreds of repositories reveals that OSS networks exhibit strong power-law distributions in influence, with the top 1\% of contributors controlling a substantial portion of network influence. Using GPU-accelerated PageRank, betweenness centrality, and custom LSTM models, we identify five distinct contributor roles: Core, Bridge, Connector, Regular, and Peripheral, each with unique network positions and structural importance. Statistical analysis reveals significant correlations between specific action types (commits, pull requests, issues) and contributor influence, with multiple regression models explaining substantial variance in influence metrics. Temporal analysis shows that network density, clustering coefficients, and modularity exhibit statistically significant temporal trends, with distinct regime changes coinciding with major project milestones. Structural integrity simulations show that Bridge contributors, despite representing a small fraction of the network, have a disproportionate impact on network cohesion when removed. Our findings provide empirical evidence for strategic contributor retention policies and offer actionable insights into community health metrics.