Understanding the Generalizability of Link Predictors Under Distribution Shifts on Graphs
<|reference_start|>Understanding the Generalizability of Link Predictors Under Distribution Shifts on Graphs: Recently, multiple models proposed for link prediction (LP) demonstrate impressive results on benchmark datasets. However, many popular benchmark datasets often assume that dataset samples are drawn from the same distribution (i.e., IID samples). In real-world situations, this assumption is often incorrect; since uncontrolled factors may lead train and test samples to come from separate distributions. To tackle the distribution shift problem, recent work focuses on creating datasets that feature distribution shifts and designing generalization methods that perform well on the new data. However, those studies only consider distribution shifts that affect {\it node-} and {\it graph-level} tasks, thus ignoring link-level tasks. Furthermore, relatively few LP generalization methods exist. To bridge this gap, we introduce a set of LP-specific data splits which utilizes structural properties to induce a controlled distribution shift. We verify the shift's effect empirically through evaluation of different SOTA LP methods and subsequently couple these methods with generalization techniques. Interestingly, LP-specific methods frequently generalize poorly relative to heuristics or basic GNN methods. Finally, this work provides analysis to uncover insights for enhancing LP generalization. Our code is available at: \href{https://github.com/revolins/LPStructGen}{https://github.com/revolins/LPStructGen}<|reference_end|>
@article{revolinsky2024understanding,
    title={Understanding the Generalizability of Link Predictors Under Distribution
  Shifts on Graphs},
    author={Jay Revolinsky, Harry Shomer, Jiliang Tang},
    journal={arXiv preprint arXiv:2406.08788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08788},
    primaryClass={cs.LG cs.AI}
}