Mixture of Link Predictors
Li Ma Haoyu Han Juanhui Li Harry Shomer Hui Liu Xiaofeng Gao Jiliang Tang
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.