  Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto}
standard in graph representation learning. However, when it comes to link
prediction, they often struggle, surpassed by simple heuristics such as Common
Neighbor (CN). This discrepancy stems from a fundamental limitation: while
MPNNs excel in node-level representation, they stumble with encoding the joint
structural features essential to link prediction, like CN. To bridge this gap,
we posit that, by harnessing the orthogonality of input vectors, pure
message-passing can indeed capture joint structural features. Specifically, we
study the proficiency of MPNNs in approximating CN heuristics. Based on our
findings, we introduce the Message Passing Link Predictor (MPLP), a novel link
prediction model. MPLP taps into quasi-orthogonal vectors to estimate
link-level structural features, all while preserving the node-level
complexities. Moreover, our approach demonstrates that leveraging
message-passing to capture structural features could offset MPNNs'
expressiveness limitations at the expense of estimation variance. We conduct
experiments on benchmark datasets from various domains, where our method
consistently outperforms the baseline methods.
