{"total":69,"rows":[{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713,"area":null},{"id":"2308.07134","title":"Language is All a Graph Needs","created":"2023-08-14","cited_by_count":220,"area":null},{"id":"2207.02505","title":"Pure Transformers are Powerful Graph Learners","created":"2022-07-06","cited_by_count":188,"area":null},{"id":"2305.17589","title":"Graph Inductive Biases in Transformers without Message Passing","created":"2023-05-27","cited_by_count":153,"area":null},{"id":"2210.06681","title":"Brain Network Transformer","created":"2022-10-13","cited_by_count":94,"area":null},{"id":"2309.00367","title":"Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark","created":"2023-09-01","cited_by_count":85,"area":null},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83,"area":null},{"id":"2210.01765","title":"One Transformer Can Understand Both 2D & 3D Molecular Data","created":"2022-10-04","cited_by_count":77,"area":null},{"id":"2308.02565","title":"SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning","created":"2023-08-03","cited_by_count":73,"area":null},{"id":"2306.13089","title":"GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning","created":"2023-05-28","cited_by_count":70,"area":null},{"id":"2301.11956","title":"On the Connection Between MPNN and Graph Transformer","created":"2023-01-27","cited_by_count":61,"area":null},{"id":"2502.14546","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","created":"2025-02-20","cited_by_count":53,"area":null},{"id":"2502.17327","title":"AnyTop: Character Animation Diffusion with Any Topology","created":"2025-02-24","cited_by_count":41,"area":null},{"id":"2407.09777","title":"Graph Transformers: A Survey","created":"2024-07-13","cited_by_count":32,"area":null},{"id":"2408.06316","title":"Body Transformer: Leveraging Robot Embodiment for Policy Learning","created":"2024-08-12","cited_by_count":32,"area":null},{"id":"2212.02229","title":"GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction","created":"2022-11-18","cited_by_count":28,"area":null},{"id":"2502.09263","title":"Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence","created":"2025-02-13","cited_by_count":26,"area":null},{"id":"2306.07812","title":"Automated 3D Pre-Training for Molecular Property Prediction","created":"2023-06-13","cited_by_count":24,"area":null},{"id":"2303.16982","title":"Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+","created":"2023-03-16","cited_by_count":22,"area":null},{"id":"2305.13987","title":"On Structural Expressive Power of Graph Transformers","created":"2023-05-23","cited_by_count":19,"area":null},{"id":"2401.00529","title":"GraphGPT: Generative Pre-trained Graph Eulerian Transformer","created":"2023-12-31","cited_by_count":17,"area":null},{"id":"2303.00579","title":"Are More Layers Beneficial to Graph Transformers?","created":"2023-03-01","cited_by_count":15,"area":null},{"id":"2306.03032","title":"Classification of Edge-dependent Labels of Nodes in Hypergraphs","created":"2023-06-05","cited_by_count":14,"area":null},{"id":"2303.10576","title":"An Efficient Subgraph GNN with Provable Substructure Counting Power","created":"2023-03-19","cited_by_count":13,"area":null},{"id":"2311.14864","title":"Effective Structural Encodings via Local Curvature Profiles","created":"2023-11-24","cited_by_count":13,"area":null},{"id":"2402.04538","title":"Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers","created":"2024-02-07","cited_by_count":12,"area":null},{"id":"2303.03951","title":"Probing Graph Representations","created":"2023-03-07","cited_by_count":10,"area":null},{"id":"2305.11424","title":"Graph Propagation Transformer for Graph Representation Learning","created":"2023-05-19","cited_by_count":9,"area":null},{"id":"2209.07869","title":"LogGD:Detecting Anomalies from System Logs by Graph Neural Networks","created":"2022-09-16","cited_by_count":8,"area":null},{"id":"2402.08450","title":"Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2304.10933","title":"Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers","created":"2023-04-21","cited_by_count":7,"area":null},{"id":"2401.16176","title":"A Survey on Structure-Preserving Graph Transformers","created":"2024-01-29","cited_by_count":7,"area":null},{"id":"2208.09224","title":"SoMoFormer: Social-Aware Motion Transformer for Multi-Person Motion Prediction","created":"2022-08-19","cited_by_count":6,"area":null},{"id":"2402.07249","title":"Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey","created":"2024-02-11","cited_by_count":6,"area":null},{"id":"2406.16853","title":"GeoMFormer: A General Architecture for Geometric Molecular Representation Learning","created":"2024-06-24","cited_by_count":6,"area":null},{"id":"2302.02947","title":"GPS++: Reviving the Art of Message Passing for Molecular Property Prediction","created":"2023-02-06","cited_by_count":5,"area":null},{"id":"2303.00613","title":"Diffusing Graph Attention","created":"2023-03-01","cited_by_count":5,"area":null},{"id":"2312.04737","title":"Efficient End-to-end Language Model Fine-tuning on Graphs","created":"2023-12-07","cited_by_count":5,"area":null},{"id":"2404.13604","title":"CKGConv: General Graph Convolution with Continuous Kernels","created":"2024-04-21","cited_by_count":5,"area":null},{"id":"2306.01705","title":"The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles","created":"2023-06-02","cited_by_count":4,"area":null},{"id":"2310.01704","title":"Transformers are efficient hierarchical chemical graph learners","created":"2023-10-02","cited_by_count":4,"area":null},{"id":"2603.16849","title":"GIST: Gauge-Invariant Spectral Transformers for Scalable Graph Neural Operators","created":"2026-03-17","cited_by_count":4,"area":null},{"id":"2207.11333","title":"Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules","created":"2022-07-22","cited_by_count":3,"area":null},{"id":"2504.12588","title":"Plain Transformers Can be Powerful Graph Learners","created":"2025-04-17","cited_by_count":3,"area":null},{"id":"2511.08368","title":"A Circular Argument : Does RoPE need to be Equivariant for Vision?","created":"2025-11-11","cited_by_count":3,"area":null},{"id":"2603.00568","title":"Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions","created":"2026-02-28","cited_by_count":3,"area":null},{"id":"2211.07970","title":"Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning","created":"2022-11-15","cited_by_count":2,"area":null},{"id":"2405.03481","title":"AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers","created":"2024-05-06","cited_by_count":2,"area":null},{"id":"2405.20121","title":"A Structure-Aware Lane Graph Transformer Model for Vehicle Trajectory Prediction","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2410.16699","title":"Graph Transformers Dream of Electric Flow","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2505.13087","title":"Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings","created":"2025-05-19","cited_by_count":2,"area":null},{"id":"2512.04963","title":"GeoPE:A Unified Geometric Positional Embedding for Structured Tensors","created":"2025-12-04","cited_by_count":2,"area":null},{"id":"2211.16712","title":"Coordinating Cross-modal Distillation for Molecular Property Prediction","created":"2022-11-30","cited_by_count":1,"area":null},{"id":"2212.10746","title":"SLGTformer: An Attention-Based Approach to Sign Language Recognition","created":"2022-12-21","cited_by_count":1,"area":null},{"id":"2508.16829","title":"Understanding and Tackling Over-Dilution in Graph Neural Networks","created":"2025-08-22","cited_by_count":1,"area":null},{"id":"2603.14152","title":"SK-Adapter: Skeleton-Based Structural Control for Native 3D Generation","created":"2026-03-14","cited_by_count":1,"area":null},{"id":"2208.11328","title":"K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation","created":"2022-08-24","cited_by_count":0,"area":null},{"id":"2311.04653","title":"Hybrid Focal and Full-Range Attention Based Graph Transformers","created":"2023-11-08","cited_by_count":0,"area":null},{"id":"2401.01233","title":"GEN: A Practical Alternative to Graph Transformers for Long-Range Graph Modeling","created":"2024-01-02","cited_by_count":0,"area":null},{"id":"2503.05031","title":"Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes","created":"2025-03-06","cited_by_count":0,"area":null},{"id":"2601.04367","title":"Graph Integrated Transformers for Community Detection in Social Networks","created":"2026-01-07","cited_by_count":0,"area":null},{"id":"2603.22854","title":"Avoiding Over-smoothing in Social Media Rumor Detection with Pre-trained Propagation Tree Transformer","created":"2026-03-24","cited_by_count":0,"area":null},{"id":"2607.04262","title":"On Preserving Geometrical Invariance for Superpixel Image Classification using Graph Transformer","created":"2026-07-05","cited_by_count":0,"area":null},{"id":"2608.14982","title":"Do Geometry-Aware Positional Encodings Help Transformers in Spatial Imperfect-Information Games?","created":"2026-08-15","cited_by_count":0,"area":null},{"id":"2608.16097","title":"Unifying Graph Neural Networks Through a Common Layer Equation","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.30152","title":"Converse and Collision-Based Achievability for Node Localization with Hybrid Distance-Spectral Graph Positional Encodings","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.04672","title":"WEECFP-SuRGE: A Position-Aware Substructure Encoding Method for Molecular Property Prediction","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2610.04850","title":"PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning","created":"2026-10-04","cited_by_count":0,"area":null},{"id":"2610.15020","title":"PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}