Link Prediction without Graph Neural Networks

Zexi Huang, Mert Kosan, Arlei Lopes da Silva, Ambuj Singh

Keywords: Link Prediction, Graph Neural Networks, Graph Learning, Topological Heuristics

TL;DR: We address key limitations of GNN-based link prediction methods in handling class imbalance and, moreover, present a simpler, more accurate, and more efficient alternative.

Abstract: Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Networks (GNNs), which are based on an attribute-centric message-passing paradigm, have become the predominant framework for link prediction. GNNs have consistently outperformed traditional topology-based heuristics, but what contributes to their performance? Are there simpler approaches that achieve comparable or better results? To answer these questions, we first identify important limitations in how GNN-based link prediction methods handle the intrinsic class imbalance of the problem---due to the graph sparsity---in their training and evaluation. Moreover, we propose Gelato, a novel topology-centric framework that applies a topological heuristic to a graph enhanced by attribute information via graph learning. Our model is trained end-to-end with an N-pair loss on an unbiased training set to address class imbalance. Experiments show that Gelato is 145% more accurate, trains 11 times faster, infers 6,000 times faster, and has less than half of the trainable parameters compared to state-of-the-art GNNs for link prediction.

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