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A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature

Author

Listed:
  • Chunjiang Liu

    (Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610299, China
    These authors contributed equally to this work.)

  • Yikun Han

    (Department of Statistics, University of Michigan, Ann Arbor, MI 48109, USA
    These authors contributed equally to this work.)

  • Haiyun Xu

    (School of Business, Shandong University of Technology, Zibo 255000, China)

  • Shihan Yang

    (Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650031, China)

  • Kaidi Wang

    (School of Business, Macau University of Science and Technology, Macau 999078, China)

  • Yongye Su

    (Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA)

Abstract

This study presents a novel approach that synergizes community detection algorithms with various Graph Neural Network (GNN) models to bolster link prediction in scientific literature networks. By integrating the Louvain community detection algorithm into our GNN frameworks, we consistently enhanced the performance across all models tested. For example, integrating the Louvain model with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains were noted when the Louvain model was paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent increase in performance—reflected in our extensive experimentation on bipartite graphs of scientific collaborations and citations—highlights the synergistic potential of combining community detection with GNNs to overcome common link prediction challenges such as scalability and resolution limits. Our findings advocate for the integration of community structures as a significant step forward in the predictive accuracy of network science models, offering a comprehensive understanding of scientific collaboration patterns through the lens of advanced machine learning techniques.

Suggested Citation

  • Chunjiang Liu & Yikun Han & Haiyun Xu & Shihan Yang & Kaidi Wang & Yongye Su, 2024. "A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature," Mathematics, MDPI, vol. 12(3), pages 1-20, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:369-:d:1325143
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    References listed on IDEAS

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    1. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    2. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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