Mixture of Link Predictors

• URL: http://arxiv.org/abs/2402.08583v1
• Date: Tue, 13 Feb 2024 16:36:50 GMT
• Title: Mixture of Link Predictors
• Authors: Li Ma, Haoyu Han, Juanhui Li, Harry Shomer, Hui Liu, Xiaofeng Gao, Jiliang Tang
• Abstract summary: Link prediction aims to forecast unseen connections in graphs. Heuristic methods, leveraging a range of different pairwise measures, often rival the performance of vanilla Graph Neural Networks (GNNs)
• Score: 40.32089688353189
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning. Heuristic methods, leveraging a range of different pairwise measures such as common neighbors and shortest paths, often rival the performance of vanilla Graph Neural Networks (GNNs). Therefore, recent advancements in GNNs for link prediction (GNN4LP) have primarily focused on integrating one or a few types of pairwise information. In this work, we reveal that different node pairs within the same dataset necessitate varied pairwise information for accurate prediction and models that only apply the same pairwise information uniformly could achieve suboptimal performance. As a result, we propose a simple mixture of experts model Link-MoE for link prediction. Link-MoE utilizes various GNNs as experts and strategically selects the appropriate expert for each node pair based on various types of pairwise information. Experimental results across diverse real-world datasets demonstrate substantial performance improvement from Link-MoE. Notably, Link-MoE achieves a relative improvement of 18.82\% on the MRR metric for the Pubmed dataset and 10.8\% on the Hits@100 metric for the ogbl-ppa dataset, compared to the best baselines.

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