GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
Heterogeneous graph transformer
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HeterSEED decouples semantics from structure in heterogeneous graphs under heterophily using separate channels and adaptive fusion, proving higher expressiveness and lower bias than standard HGNNs while outperforming baselines on large graphs.
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Gaussian Relational Graph Transformer
GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.
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HeterSEED: Semantics-Structure Decoupling for Heterogeneous Graph Learning under Heterophily
HeterSEED decouples semantics from structure in heterogeneous graphs under heterophily using separate channels and adaptive fusion, proving higher expressiveness and lower bias than standard HGNNs while outperforming baselines on large graphs.