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

TL;DR

This paper introduces a method combining community detection and Graph Neural Networks to improve link prediction in scientific literature networks, demonstrating significant performance gains across multiple models.

Contribution

The study innovatively integrates Louvain community detection with GNNs, enhancing link prediction accuracy in scientific collaboration and citation networks.

Findings

Louvain integration improves GNN AUC scores from 0.777 to 0.823

Consistent performance improvements across various GNN architectures

Highlights the importance of community structures in network link prediction

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 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.