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 enhance performance across all models tested. For example, integrating Louvain with the GAT model resulted in an AUC score increase from 0.777 to 0.823, exemplifying the typical improvements observed. Similar gains are noted when Louvain is paired with other GNN architectures, confirming the robustness and effectiveness of incorporating community-level insights. This consistent uplift 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.

翻译：本研究提出了一种创新方法，将社区检测算法与多种图神经网络模型协同结合，以增强科学文献网络中的链接预测能力。通过将Louvain社区检测算法集成到我们的GNN框架中，所有测试模型的性能均得到持续提升。例如，将Louvain与GAT模型结合后，AUC分数从0.777提升至0.823，充分体现了典型的改进效果。当Louvain与其他GNN架构配合使用时，同样观察到类似的性能增益，这证实了融入社区级别信息具有鲁棒性与有效性。我们针对科学合作与引文二部图开展的广泛实验表明，这种性能的持续提升揭示了将社区检测与GNN相结合以克服链接预测中可扩展性及分辨率限制等常见挑战的巨大协同潜力。研究结果主张将社区结构整合作为提升网络科学模型预测准确性的重要进展，并通过先进机器学习技术的视角，为理解科学合作模式提供了全面洞见。