Title: Improving expressivity in Link Prediction with GNNs via the Shortest Path
Primary area: learning on graphs and other geometries & topologies
Keywords: graph neural networks, expressivity, shortest path

Abstract: Graph Neural Networks (GNNs) often fail to capture the link-specific structural patterns essential for accurate link prediction, since their node-centric message passing might overlook the subgraph structures connecting two nodes. Prior attempts to inject such structural context either suffer from high computational cost or rely on oversimplified heuristics (e.g., common neighbor counts) that cannot capture multi-hop dependencies. We propose SP4LP (Shortest Path for Link Prediction), a new framework that integrates GNN-based node encodings with sequence modelling over shortest paths. Specifically, SP4LP first computes node representations with a GNN, then extracts the shortest path between each candidate node pair and processes the sequence of node embeddings with a sequence model. This design allows SP4LP to efficiently capture expressive multi-hop relational patterns. Theoretically, we show that SP4LP is strictly more expressive than both standard message-passing GNNs and several leading structural feature methods, positioning it as a general and principled framework for link prediction in graphs. Empirically, SP4LP sets a new state of the art on many standard link prediction benchmarks.