Link Prediction without Graph Neural Networks

• URL: http://arxiv.org/abs/2305.13656v1
• Date: Tue, 23 May 2023 03:59:21 GMT
• Title: Link Prediction without Graph Neural Networks
• Authors: Zexi Huang, Mert Kosan, Arlei Silva, Ambuj Singh
• Abstract summary: Link prediction is a fundamental task in many graph applications. Graph Neural Networks (GNNs) have become the predominant framework for link prediction. We propose Gelato, a novel framework that applies a topological-centric framework to a graph enhanced by attribute information via graph learning.
• Score: 7.436429318051601
• License: http://creativecommons.org/licenses/by/4.0/
• 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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