Understanding the Design Principles of Link Prediction in Directed Settings
Keywords: directed link prediction, graph representation learning, link prediction heuristics, directed graph neural networks
Abstract: Link prediction is a widely studied task in Graph Representation Learning (GRL) for modeling relational data. 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 involves 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 the undirected settings. We propose simple but effective adaptations of these heuristics to the directed link prediction task and demonstrate that these modifications yield competitive performance compared to 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.
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 2245
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