Graph Transformer

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Overview

Graph Transformers study how attention-based models can learn from nodes, edges, and other relationships while respecting graph structure. Common approaches add positional or structural encodings—such as Laplacian spectra, random walks, or graph distances—or use local, sparse, hierarchical, and relation-aware attention to control computation. They are applied to tasks including molecular prediction, temporal and relational graphs, scientific modeling, and graph generation, with evaluations spanning standard graph benchmarks and larger or shifted settings. A central tension is how to preserve useful structural biases and generalize across graph sizes while scaling attention; theory also indicates that global attention alone does not necessarily overcome the expressive limits of common graph architectures.

Latest papers

CardsList

101 papers17 papers in the last four weeks, up 89% on the four weeks before. 0.1% of all new papers.

  1. Joint Dynamic Lane Reversal and Variable Speed Limit Control: A Graphormer-Based Approach

    Oct 26, 2026Hamed Araab, Abbas Ahmadi, Mohammad Hossein BasiriIntelligent Transportation SystemsGraph Transformer

    Department of Industrial Engineering and Management Systems, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran · Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

  2. Efficient Edge-aware Attention Network for Graph Generation

    Oct 20, 2026Vincent Jung, Alba Carballo-Castro, Yiming Qin +2Graph GenerationGraph Transformer

    LTS4, EPFL, Lausanne, Switzerland · Università della Svizzera italiana, Lugano, Switzerland

  3. ProtoGTX: Prototype-Aligned Graph Transformer for Explainable Whole-Slide Image Analysis

    Oct 16, 2026Iori Azuma, Hiroyuki Kusuhara, Tadahaya MizunoExplainable Medical Image AnalysisWhole-Slide Image Analysis

    Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan · The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Japan

  4. Why don't Graph Transformers attend to the same token?

    Oct 14, 2026Alexandre BismuthAttention SinksAttention Mechanisms

    BOLD Lab University of Oxford

  5. Neuro-Symbolic Graph Transformers for Hierarchical Intention Recognition from Multimodal Human Activity Sequences

    Oct 13, 2026Farnaz Soleimani, Abdelghani Chibani, Yacine Amirat +1Human Activity RecognitionGraph Transformer

    LISSI

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

    Oct 12, 2026Yuxiang Zeng, Kun Xie, Yang Wang +5Neural Architecture Performance PredictionGraph Transformer

    Hunan University · Hunan University of Science and Technology · Chinese Academy of Sciences +1

  7. MERIT: Scaling KG Foundation Models with Relation Transformers

    Oct 11, 2026Cosimo Gregucci, Daniel Hernandez, Antonio Vergari +1Graph Foundation ModelsKG Completion

    Institute for Artificial Intelligence, University of Stuttgart, Germany · School of Informatics, University of Edinburgh, UK · University of Southampton

  8. Learning to Extrapolate in Depth with Looped Transformers

    Oct 10, 2026Huitao Yang, Jianhao Huang, Zixuan Wang +1Position ExtrapolationRecurrent Transformers

    University of California, Los Angeles · Princeton University

  9. Global Communication or Graph-Specific Memory?

    Oct 5, 2026Hamed Shirzad, Danica J. SutherlandGraph Neural NetworksTransductive Learning

    University of British Columbia

  10. Efficient Graph Generation via Direct Prediction and Flow Matching

    Oct 4, 2026Susie LuFlow MatchingGraph Generation

    MIT

  11. Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

    Oct 4, 2026Avi Epstein, Snir Nehemia, Haim Suchowski +1Physics-Informed GNNsGraph Transformer

    Tel Aviv University

  12. Dual-Scale Relational Graph Transformers for Ecosystem-Aware Fraud Detection

    Oct 2, 2026Mohsen Nayebi Kerdabadi, Xinrou Li, Yao Xiao +2Financial Fraud DetectionGraph Transformer

    University of Kansas, Lawrence, KS, USA · AI Foundations, Capital One, McLean, VA, USA

  13. Higher-Order Positional Encodings for Graph Representation Learning

    Oct 1, 2026Caleb Stam, Aagrim Hoysal, Sanjukta KrishnagopalGraph Representation LearningPositional Encoding

    University of California, Santa Barbara

  14. Stable Transformers for Graph Generation

    Sep 30, 2026Luca Miglior, Alessio Gravina, Davide BacciuRepresentation CollapseGraph Generation

    Department of Computer Science, University of Pisa, Italy

  15. Information Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis

    Sep 29, 2026Jingxi Feng, Xudong Chen, Yifan Zhang +5Medical DiagnosisInformation Bottleneck

  16. cktFormer: Transformer-Based Approach for Automated Analog Circuit Design

    Sep 29, 2026Pasindu Dodampegama, Praveen Wijesinghe, Naveen Basnayake +2Electronic Design AutomationGraph Transformer

    University of Moratuwa, Sri Lanka · Independent Researcher, Victoria, Australia

  17. Attention Graphons: A Graph Limit Perspective on Graph Transformers

    Sep 28, 2026Caio F. Deberaldini Netto, Moshe Eliasof, Luana RuizGraphon TheoryTransformer Attention

    Johns Hopkins University Baltimore, MD · University of Cambridge Cambridge, UK

  18. MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism

    Sep 28, 2026Tong Qiao, Ao Zhou, Yingjie Qi +2Distributed OptimizationHigh-Performance Computing

    School of Computer Science and Engineering, Beihang University, China · School of Software, Beihang University, China · State Key Laboratory of Complex and Critical Software Environment, Beihang University, China +1

  19. Predicting the Financial Impact of Supply Chain Risk for Major AI-Related Semiconductor Firms: A Heterogeneous Graph Patch Transformer Approach

    Sep 26, 2026Jianna Hur, Sagar SamtaniFinancial ForecastingSupply Chain Management

    Data Science and Artificial Intelligence Lab (DSAIL), Kelley School of Business Indiana University, Bloomington, IN, USA

  20. Analytic-Walk Rotary Positional Encodings for Graphs

    Sep 26, 2026Jiaqing Xie, Yuxin Wang, Xipeng QiuGraph Positional EncodingRotary Positional Embeddings

    Shanghai Innovation Institute · Fudan University

  21. QUARTET: Quad-branch cross-Attention and Random-walk Traces for Enhancing Transformers on Relational Graphs

    Sep 22, 2026Kyaw Hpone Myint, Nan Jiang, Xiang Li +4Temporal GNNsTransformer Attention

    Capital One

  22. Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control

    Sep 13, 2026Sergei, KomarovFinancial Fraud DetectionGraph Transformer

    JP Morgan New York, USA

  23. Pre-training with Graph Transformers

    Sep 12, 2026Jiaming Wang, Thomas Laurent, Xavier BressonGraph TransformerGraph Foundation Models

    National University of Singapore Singapore · Loyola Marymount University Los Angeles, USA

  24. GSF-χ\text{GSF-}χ: Global Stereochemical Fields for Chiral Graph Transformers

    Sep 11, 2026Jiaqing Xie, Yuxin Wang, Xipeng QiuRotary Positional EmbeddingsMolecular Property Prediction

    Shanghai Innovation Institute · Fudan University

  25. ONE CYLinder: A Benchmark for Graph-Based Surrogate Modeling of Unsteady Bluff-Body Flows

    Sep 8, 2026Théodore Michel, Antoine Campos, Alban Dujardin +3Surrogate ModelingPDE Surrogate Modeling

    Mines Paris PSL University Centre for Material Forming (CEMEF) UMR CNRS, Sophia Antipolis, 06904, France

  26. GraphNOSE: A Graph Transformer in Olfaction

    Sep 4, 2026Mrityunjay Sharma, Sarabeshwar Balaji, Valentina Parma +1Molecular Property PredictionMolecular Representation Learning

    CSIR- Central Scientific Instruments Organisation, Sector 30-C, Chandigarh-160030, India · Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India · Department of Higher Education,Himachal Pradesh,Shimla -171001,India +3

  27. WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction

    Sep 4, 2026Robert EppsMolecular Representation LearningMolecular Property Prediction

    Eli Lilly and Company Lilly Corporate Center, Indianapolis, IN, United States of America

  28. AcroMELD: Recovering Interactive PDF Forms with Structure-Aware Graph Set Transformers

    Aug 23, 2026Samuel AbramovDocument Image UnderstandingDocument Layout Analysis

    Accessful GmbH

  29. VisAdj: Learning Adjacency Matrices from Node-Link Images

    Aug 22, 2026Jiahao Xie, Guangmo TongGraph ReconstructionGraph Structure Learning

    University of Delaware Newark, Delaware, USA

  30. DyG2^2T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer

    Aug 19, 2026Yansong Wang, Zhaobo Qi, Xinyan Liu +4Trajectory PredictionMotion Representation Learning

    School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China · Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China · School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 101408, China +1