This study introduces an innovative approach that integrates community detection algorithms with Graph Neural Network (GNN) models to enhance link prediction in scientific literature networks. We specifically focus on the utilization of the Louvain community detection algorithm to uncover latent community structures within these networks, which are then incorporated into GNN architectures to predict potential links. Our methodology demonstrates the importance of understanding community dynamics in complex networks and leverages the strengths of both community detection and GNNs to improve predictive accuracy. Through extensive experiments on bipartite graphs representing scientific collaborations and citations, our approach not only highlights the synergy between community detection and GNNs but also addresses some of the prevalent challenges in link prediction, such as scalability and resolution limits. The results suggest that incorporating community-level information can significantly enhance the performance of GNNs in link prediction tasks. This work contributes to the evolving field of network science by offering a novel perspective on integrating advanced machine learning techniques with traditional network analysis methods to better understand and predict the intricate patterns of scientific collaborations.

翻译：本研究提出一种创新方法，将社区检测算法与图神经网络（GNN）模型相结合，以提升科学文献网络中的链接预测性能。我们重点利用Louvain社区检测算法揭示这些网络中的潜在社区结构，并将其融入GNN架构中预测潜在链接。该方法强调了理解复杂网络中社区动态的重要性，并充分利用社区检测与GNN的优势来提高预测精度。通过在反映科研合作与引文关系的二分图上进行大量实验，本方法不仅展示了社区检测与GNN之间的协同效应，还解决了链接预测中普遍存在的可扩展性与分辨率限制等挑战。结果表明，融入社区级信息可显著提升GNN在链接预测任务中的表现。本研究通过提供融合先进机器学习技术与传统网络分析方法的全新视角，有助于深入理解并预测科研合作的复杂模式，为网络科学领域的发展作出贡献。