J* E* C* N* U* N* S* ›› 2025, Vol. 2025 ›› Issue (5): 109-124.doi: 10.3969/j.issn.1000-5641.2025.05.011

• Open Source Ecosystem: Development and Governance • Previous Articles    

Open-source collaboration structure modeling and multilayer-network link-prediction methods

Pu ZHAO1(), Qingxi PENG1,*(), Yuang ZHANG2, Xiejie JIN1, Dezhou ZHAO3   

  1. 1. School of Information Engineering, Wuhan College, Wuhan 430210, China
    2. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
    3. Ministry of Basic Education, Zaozhuang Technician College, Zaozhuang, Shandong 277000, China
  • Received:2025-06-27 Accepted:2025-07-31 Online:2025-09-25 Published:2025-09-25
  • Contact: Qingxi PENG E-mail:zhaopu_2025@qq.com;695979317@qq.com

Abstract:

Collaborative relationships among open-source projects are becoming increasingly complex, involving multiple reuse mechanisms such as dependency co-usage, language consistency, and contributor overlap. Traditional graph models struggle to represent these heterogeneous structures in a unified manner, limiting their ability to identify potential collaboration links. This paper proposes an analytical framework that integrates multilayer graph modeling with structure-aware link prediction, tailored to open-source ecosystems. A three-layer unweighted graph is constructed to capture different types of collaborations, and two structural enhancements—layer overlap modulation and community-aware scoring—are introduced to improve structural perception and semantic interpretability. Experimental results on multiple real-world datasets show that the proposed method consistently outperforms mainstream link prediction algorithms, particularly in networks with high structural heterogeneity. Further analysis reveals that the predicted links exhibit strong community consistency and semantic recoverability. Overall, the proposed approach effectively uncovers latent collaboration paths among open-source projects and provides structural support for reuse modeling and community evolution analysis.

Key words: open-source collaboration network, multilayer network modeling, link prediction, community detection

CLC Number: