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GraphNOSE is an open-source graph transformer framework that predicts multi-label odor descriptors from molecular structures, achieving superior performance over existing methods with fewer parameters and improved generalization to out-of-distribution compounds.
This paper introduces MiGHT-EHR, a multi-task graph transformer for heterogeneous temporal EHR data, jointly modeling clinical entities, temporal trajectories, and task dependencies. It outperforms state-of-the-art methods on MIMIC-III and MIMIC-IV across drug recommendation, length-of-stay, mortality, and readmission prediction.
THGFM is a dual-branch temporal heterogeneous graph fusion model that combines shared-space and relation-type-partitioned attention with rotary temporal attention, outperforming baselines on academic graph benchmarks.
X-LogSMask introduces a logarithmic structural mask for graph transformers, injecting graph topology directly into attention logits to achieve state-of-the-art performance on 13 out of 20 benchmarks while preserving interpretability and multi-hop information propagation.
This paper introduces RelGT-AC, a relational graph transformer architecture tailored for autocomplete tasks in relational databases. The model extends the RelGT architecture with column masking to prevent trivial solutions, a unified task head for multiple prediction types, and a TF-IDF text encoder to leverage lexical signals, achieving significant improvements over baselines on RelBench v2 benchmarks.