Node Duplication Improves Cold-start Link Prediction
<|reference_start|>Node Duplication Improves Cold-start Link Prediction: Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite their overall strong performance. In practical applications of LP, like recommendation systems, improving performance on low-degree nodes is critical, as it amounts to tackling the cold-start problem of improving the experiences of users with few observed interactions. In this paper, we investigate improving GNNs' LP performance on low-degree nodes while preserving their performance on high-degree nodes and propose a simple yet surprisingly effective augmentation technique called NodeDup. Specifically, NodeDup duplicates low-degree nodes and creates links between nodes and their own duplicates before following the standard supervised LP training scheme. By leveraging a ''multi-view'' perspective for low-degree nodes, NodeDup shows significant LP performance improvements on low-degree nodes without compromising any performance on high-degree nodes. Additionally, as a plug-and-play augmentation module, NodeDup can be easily applied to existing GNNs with very light computational cost. Extensive experiments show that NodeDup achieves 38.49%, 13.34%, and 6.76% improvements on isolated, low-degree, and warm nodes, respectively, on average across all datasets compared to GNNs and state-of-the-art cold-start methods.<|reference_end|>
@article{guo2024node,
    title={Node Duplication Improves Cold-start Link Prediction},
    author={Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil
  Shah, Nitesh V. Chawla},
    journal={arXiv preprint arXiv:2402.09711},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09711},
    primaryClass={cs.LG cs.SI}
}