mlsanityData to 7 Oct 2026

Beyond Code Contributions: How Network Position, Temporal Bursts, and Code Review Activities Shape Contributor Influence in Large-Scale Open Source Ecosystems

arXiv 2602.06426 ↗First posted 6 Feb 2026cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 6 Feb 2026.

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10 of 60 references.

References · 10

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
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  7. 2020Detecting and Characterizing Bots that Commit Codecited by 10
  8. 2023Emoji Promotes Developer Participation and Issue Resolution on GitHubcited by 6
  9. 2022All That's Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Redditcited by 4
  10. 2023Team Resilience under Shock: An Empirical Analysis of GitHub Repositories during Early COVID-19 Pandemiccited by 3

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