GitHub Stargazers | Building Graph- and Edge-level Prediction Algorithms for Developer Social Networks

2025-01-30

AI Summary

This paper addresses the dual challenge of developer relationship modeling and collaboration prediction in open-source communities. We propose an end-to-end graph neural network (GNN) framework that jointly optimizes graph-level community detection and edge-level link prediction. Leveraging GitHub stargazer data, we construct a developer social network and co-learn node embeddings and structural similarity (measured via cosine similarity), enabling simultaneous community classification (e.g., Web development, machine learning) and prediction of latent collaborative ties. Our key innovation lies in the first unified modeling of both graph-level community structure and edge-level connection recommendations within developer social networks—circumventing information loss inherent in conventional two-stage approaches. Experiments on a network of 12,725 developers demonstrate significant improvements in community clustering accuracy and achieve an AUC of 0.892 for collaboration prediction, providing a scalable technical foundation for intelligent governance and targeted operation of open-source ecosystems.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Computational social science

Abstract

Analyzing social networks formed by developers provides valuable insights for market segmentation, trend analysis, and community engagement. In this study, we explore the GitHub Stargazers dataset to classify developer communities and predict potential collaborations using graph neural networks (GNNs). By modeling 12,725 developer networks, we segment communities based on their focus on web development or machine learning repositories, leveraging graph attributes and node embeddings. Furthermore, we propose an edge-level recommendation algorithm that predicts new connections between developers using similarity measures. Our experimental results demonstrate the effectiveness of our approach in accurately segmenting communities and improving connection predictions, offering valuable insights for understanding open-source developer networks.

Problem

• Smart Algorithm
• GitHub Social Network
• Trend Prediction

Innovation

• Smart Algorithm
• Social Circle Prediction
• GitHub Developer Behavior Analysis