Node Duplication Improves Cold-start Link Prediction
Zhichun Guo Tong Zhao Yozen Liu Kaiwen Dong William Shiao Neil Shah Nitesh V. Chawla
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.