Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach

• URL: http://arxiv.org/abs/2402.14802v1
• Date: Thu, 22 Feb 2024 18:56:31 GMT
• Title: Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach
• Authors: Andrea Giuseppe Di Francesco, Francesco Caso, Maria Sofia Bucarelli and Fabrizio Silvestri
• Abstract summary: We introduce GRAFF-LP, an extension of GRAFF to link prediction. We evaluate its efficacy within a recent collection of heterophilic graphs.
• Score: 5.187216033152917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the past years, Graph Neural Networks (GNNs) have become the `de facto' standard in various deep learning domains, thanks to their flexibility in modeling real-world phenomena represented as graphs. However, the message-passing mechanism of GNNs faces challenges in learnability and expressivity, hindering high performance on heterophilic graphs, where adjacent nodes frequently have different labels. Most existing solutions addressing these challenges are primarily confined to specific benchmarks focused on node classification tasks. This narrow focus restricts the potential impact that link prediction under heterophily could offer in several applications, including recommender systems. For example, in social networks, two users may be connected for some latent reason, making it challenging to predict such connections in advance. Physics-Inspired GNNs such as GRAFF provided a significant contribution to enhance node classification performance under heterophily, thanks to the adoption of physics biases in the message-passing. Drawing inspiration from these findings, we advocate that the methodology employed by GRAFF can improve link prediction performance as well. To further explore this hypothesis, we introduce GRAFF-LP, an extension of GRAFF to link prediction. We evaluate its efficacy within a recent collection of heterophilic graphs, establishing a new benchmark for link prediction under heterophily. Our approach surpasses previous methods, in most of the datasets, showcasing a strong flexibility in different contexts, and achieving relative AUROC improvements of up to 26.7%.

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