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arXivGraph transformers

Let the Heads Talk: Beyond Diagonal Graph Attention

Top-A learns edge-conditioned off-diagonal cross-head routes in multi-head attention that preserve diagonal paths, improving interaction-dependent tasks without benefiting heterophily.

Riccardo Ali, Alessio Borgi, Mario Severino, Alessio Gravina and 3 more

Published Oct 1, 2026 · 0 citations

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11/20 AI panelreviewers recommend it

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AI panel: 11 of 20 reviewers recommend it
lenient 2/5
medium 8/10
strict 1/5
59%Worth a look
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KDD 2026NankaiBeihangGraph transformers

Invariant Graph Transformer for Out-of-Distribution Generalization

GOODFormer improves graph transformer out-of-distribution generalization by jointly learning invariant predictive subgraphs, evolving positional encodings, and invariant representations.

Tianyin Liao, Ziwei Zhang, Yufei Sun, Chunyu Hu and 1 more

Published Apr 20, 2026 · 0 citations

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 2/5
medium 5/10
strict 0/5
45%Niche pick
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NeurIPS 2026JilinJilin University, ChinaGraph transformers

DualSAT: A Dual-Branch GNN-Transformer Framework for SAT Solving

Wenzhu Yang, Zhanshan Li, Jingyao Li

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026YaleThe Chinese University of Hong KGraph transformers

Busemannformer: Horospherical Self-Attention for Hyperbolic Graph Transformers

Youheng Yao, Ziyao Zeng, Wenbo Liao, Tianqi Wang

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026HunanHunan University of Science and , Chinese Academy of SciencesCNIC CASHong Kong PolytechnicGraph transformers

DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning

Yuxiang Zeng, Kun Xie, Yang Wang, Jigang Wen and 4 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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88%Must read
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NeurIPS 2026IBM Research AIIBM Research ETH ZürichInternational Business MachinesGraph transformers

GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators

GIST proposes gauge-invariant spectral transformers that use efficient spectral embeddings to achieve linear complexity and provable discretization-invariance, setting state-of-the-art on large-scale mesh benchmarks.

Mattia Rigotti, Nicholas Thumiger, Thomas Frick

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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15/20 AI panelreviewers recommend it

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 10/10
strict 2/5