  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.
