@inproceedings{
ma2024mixture,
title={Mixture of Link Predictors on Graphs},
author={Li Ma and Haoyu Han and Juanhui Li and Harry Shomer and Hui Liu and Xiaofeng Gao and Jiliang Tang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=X3oeoyJlMw}
}
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-Mo achieves a relative improvement of 18.71% on the MRR metric for the Pubmed dataset and 9.59% on the Hits@100 metric for the ogbl-ppa dataset, compared to the best baselines. The code is available at https://github.com/ml-ml/Link-MoE/.
Mixture of Link Predictors on Graphs
Li Ma Haoyu Han Juanhui Li Harry Shomer Hui Liu Xiaofeng Gao Jiliang Tang