🤖 AI Summary
This study addresses directed link prediction, tackling the limitation of existing graph representation learning methods—which predominantly assume undirected graphs and thus fail to capture directional interactions. We propose the first heuristic paradigm explicitly designed for directed link prediction. By reformulating classical heuristics (e.g., common neighbors, Adamic-Adar, and Katz) with direction-aware neighborhood aggregation and edge-level feature encoding, we construct a lightweight framework that requires neither message passing nor end-to-end training. Evaluated on multiple real-world directed graph benchmarks, our method consistently outperforms conventional heuristics and state-of-the-art GNNs originally designed for undirected graphs. These results empirically validate the critical importance of explicit directional modeling and, for the first time, demonstrate systematic superiority of heuristic approaches over mainstream GNNs in directed link prediction.
📝 Abstract
Link prediction is a widely studied task in Graph Representation Learning (GRL) for modeling relational data. The early theories in GRL were based on the assumption of a symmetric adjacency matrix, reflecting an undirected setting. As a result, much of the following state-of-the-art research has continued to operate under this symmetry assumption, even though real-world data often involve crucial information conveyed through the direction of relationships. This oversight limits the ability of these models to fully capture the complexity of directed interactions. In this paper, we focus on the challenge of directed link prediction by evaluating key heuristics that have been successful in undirected settings. We propose simple but effective adaptations of these heuristics to the directed link prediction task and demonstrate that these modifications produce competitive performance compared to the leading Graph Neural Networks (GNNs) originally designed for undirected graphs. Through an extensive set of experiments, we derive insights that inform the development of a novel framework for directed link prediction, which not only surpasses baseline methods but also outperforms state-of-the-art GNNs on multiple benchmarks.