Gaussian Relational Graph Transformer

• URL: http://arxiv.org/abs/2605.15575v1
• Date: Fri, 15 May 2026 03:36:28 GMT
• Title: Gaussian Relational Graph Transformer
• Abstract summary: GelGT is a relational graph transformer with a structure-semantic collaborative sampling strategy.<n>Experiments on various real-world datasets demonstrate that GelGT achieves state-of-theart downstream task performance.
• Score: 14.982543038240784
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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