  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.
