  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}
