Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods

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This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of existing link prediction methods.

• Our analysis reveals that GNNs cannot effectively learn structural information related to the number of common neighbors between two nodes, primarily due to the nature of set-based pooling of the neighborhood aggregation scheme.
• Also, our extensive experiments indicate that trainable node embeddings can improve the performance of GNN-based link prediction models.
• Importantly, we observe that the denser the graph, the greater such the improvement.