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.

翻译：链接预测是图机器学习中的基本任务，旨在预测图中未观测到的连接。基于启发式方法，利用共同邻居、最短路径等不同成对度量指标，往往能与普通图神经网络（GNN）性能相媲美。因此，近期面向链接预测的图神经网络（GNN4LP）研究主要聚焦于整合一种或少数几种成对信息。本研究发现，同一数据集中不同节点对需要不同的成对信息才能实现准确预测，而仅统一应用相同成对信息的模型可能达到次优性能。为此，我们提出一种简单的专家混合模型Link-MoE用于链接预测。Link-MoE采用多种GNN作为专家，并基于不同类型的成对信息为每个节点对策略性地选择最合适的专家。在多个真实世界数据集上的实验结果表明，Link-MoE实现了显著的性能提升。值得注意的是，相较于最优基线模型，Link-MoE在Pubmed数据集上的MRR指标相对提升18.82%，在ogbl-ppa数据集上的Hits@100指标相对提升10.8%。