Gaussian Relational Graph Transformer
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Computer Science > Machine Learning
Title:Gaussian Relational Graph Transformer
Abstract:Relational graph learning models relational databases as graphs and has demonstrated superior performance on a wide range of relational predictive tasks. However, existing methods struggle to capture long-range dependencies due to information decay in their message-passing mechanisms, and recent relational graph transformers remain limited in jointly modeling structural, semantic, and temporal information. In this paper, we propose GelGT, a Gaussian relational graph transformer that explicitly addresses these challenges. GelGT introduces a structure-semantic collaborative sampling strategy to preserve structural connectivity while filtering irrelevant semantic information, and incorporates a Gaussian graph attention mechanism with a learnable Gaussian bias on the sampled subgraphs to dynamically encode temporal dependencies. Extensive experiments on various real-world datasets demonstrate that GelGT achieves state-of-the-art downstream task performance, with up to a 13.8% improvement in predictive performance.
| Subjects: | Machine Learning (cs.LG); Databases (cs.DB) |
| Cite as: | arXiv:2605.15575 [cs.LG] |
| (or arXiv:2605.15575v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15575
arXiv-issued DOI via DataCite (pending registration)
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