  Graph Neural Networks (GNNs) have been widely applied to various fields for
learning over graph-structured data. They have shown significant improvements
over traditional heuristic methods in various tasks such as node classification
and graph classification. However, since GNNs heavily rely on smoothed node
features rather than graph structure, they often show poor performance than
simple heuristic methods in link prediction where the structural information,
e.g., overlapped neighborhoods, degrees, and shortest paths, is crucial. To
address this limitation, we propose Neighborhood Overlap-aware Graph Neural
Networks (Neo-GNNs) that learn useful structural features from an adjacency
matrix and estimate overlapped neighborhoods for link prediction. Our Neo-GNNs
generalize neighborhood overlap-based heuristic methods and handle overlapped
multi-hop neighborhoods. Our extensive experiments on Open Graph Benchmark
datasets (OGB) demonstrate that Neo-GNNs consistently achieve state-of-the-art
performance in link prediction. Our code is publicly available at
https://github.com/seongjunyun/Neo_GNNs.
