{"total":69,"rows":[{"id":"2308.07134","title":"Language is All a Graph Needs","created":"2023-08-14","cited_by_count":220,"area":null},{"id":"2306.10759","title":"SGFormer: Simplifying and Empowering Transformers for Large-Graph Representations","created":"2023-06-19","cited_by_count":133,"area":null},{"id":"2212.13350","title":"A Generalization of ViT/MLP-Mixer to Graphs","created":"2022-12-27","cited_by_count":93,"area":null},{"id":"2406.08993","title":"Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification","created":"2024-06-13","cited_by_count":85,"area":null},{"id":"2403.01232","title":"Polynormer: Polynomial-Expressive Graph Transformer in Linear Time","created":"2024-03-02","cited_by_count":65,"area":null},{"id":"2303.06818","title":"Backdoor Defense via Deconfounded Representation Learning","created":"2023-03-13","cited_by_count":38,"area":null},{"id":"2210.15304","title":"Explaining the Explainers in Graph Neural Networks: a Comparative Study","created":"2022-10-27","cited_by_count":35,"area":null},{"id":"2403.16030","title":"VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections","created":"2024-03-24","cited_by_count":34,"area":null},{"id":"2505.10960","title":"Relational Graph Transformer","created":"2025-05-16","cited_by_count":34,"area":null},{"id":"2407.09777","title":"Graph Transformers: A Survey","created":"2024-07-13","cited_by_count":32,"area":null},{"id":"2404.17169","title":"FairGT: A Fairness-aware Graph Transformer","created":"2024-04-26","cited_by_count":29,"area":null},{"id":"2305.13997","title":"Learning Subpocket Prototypes for Generalizable Structure-based Drug Design","created":"2023-05-22","cited_by_count":25,"area":null},{"id":"2411.05815","title":"Graph Neural Networks for Financial Fraud Detection: A Review","created":"2024-11-01","cited_by_count":25,"area":null},{"id":"2506.16654","title":"Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures","created":"2025-06-19","cited_by_count":23,"area":null},{"id":"2310.04987","title":"Data-centric Graph Learning: A Survey","created":"2023-10-08","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":"2305.02866","title":"Hierarchical Transformer for Scalable Graph Learning","created":"2023-05-04","cited_by_count":17,"area":null},{"id":"2406.14021","title":"HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment","created":"2024-06-20","cited_by_count":17,"area":null},{"id":"2308.11129","title":"Enhancing Graph Transformers with Hierarchical Distance Structural Encoding","created":"2023-08-22","cited_by_count":16,"area":null},{"id":"2308.09517","title":"Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity","created":"2023-08-18","cited_by_count":14,"area":null},{"id":"2406.01977","title":"What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding","created":"2024-06-04","cited_by_count":14,"area":null},{"id":"2407.11907","title":"GraphFM: A generalist graph transformer that learns transferable representations across diverse domains","created":"2024-07-16","cited_by_count":14,"area":null},{"id":"2502.16533","title":"A Survey of Graph Transformers: Architectures, Theories and Applications","created":"2025-02-23","cited_by_count":14,"area":null},{"id":"2402.02005","title":"Topology-Informed Graph Transformer","created":"2024-02-03","cited_by_count":13,"area":null},{"id":"2402.12535","title":"Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics","created":"2024-02-19","cited_by_count":12,"area":null},{"id":"2304.12436","title":"An Equivariant Generative Framework for Molecular Graph-Structure Co-Design","created":"2023-04-12","cited_by_count":10,"area":null},{"id":"2311.04434","title":"A Hierarchical Spatial Transformer for Massive Point Samples in Continuous Space","created":"2023-11-08","cited_by_count":10,"area":null},{"id":"2312.11109","title":"Graph Transformers for Large Graphs","created":"2023-12-18","cited_by_count":10,"area":null},{"id":"2406.19258","title":"Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2412.13842","title":"Graph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training","created":"2024-12-18","cited_by_count":9,"area":null},{"id":"2410.06746","title":"Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention","created":"2024-10-09","cited_by_count":8,"area":null},{"id":"2312.05526","title":"Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection","created":"2023-12-09","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":"2407.03953","title":"Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training","created":"2024-07-04","cited_by_count":7,"area":null},{"id":"2406.09675","title":"A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness","created":"2024-06-14","cited_by_count":6,"area":null},{"id":"2505.02027","title":"GraphPrompter: Multi-stage Adaptive Prompt Optimization for Graph In-Context Learning","created":"2025-05-04","cited_by_count":6,"area":null},{"id":"2505.23185","title":"Improving the Effective Receptive Field of Message-Passing Neural Networks","created":"2025-05-29","cited_by_count":6,"area":null},{"id":"2305.07521","title":"AGFormer: Efficient Graph Representation with Anchor-Graph Transformer","created":"2023-05-12","cited_by_count":5,"area":null},{"id":"2310.11025","title":"SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning","created":"2023-10-17","cited_by_count":5,"area":null},{"id":"2401.15203","title":"FedGT: Federated Node Classification with Scalable Graph Transformer","created":"2024-01-26","cited_by_count":5,"area":null},{"id":"2406.15852","title":"Next Level Message-Passing with Hierarchical Support Graphs","created":"2024-06-22","cited_by_count":5,"area":null},{"id":"2406.19249","title":"NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification","created":"2024-06-27","cited_by_count":5,"area":null},{"id":"2502.08101","title":"Rethinking Tokenized Graph Transformers for Node Classification","created":"2025-02-12","cited_by_count":5,"area":null},{"id":"2305.12677","title":"Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs","created":"2023-05-22","cited_by_count":4,"area":null},{"id":"2406.12059","title":"A Scalable and Effective Alternative to Graph Transformers","created":"2024-06-17","cited_by_count":4,"area":null},{"id":"2408.07340","title":"Towards Few-shot Self-explaining Graph Neural Networks","created":"2024-08-14","cited_by_count":4,"area":null},{"id":"2312.05479","title":"Exploring Sparsity in Graph Transformers","created":"2023-12-09","cited_by_count":3,"area":null},{"id":"2403.15480","title":"SpikeGraphormer: A High-Performance Graph Transformer with Spiking Graph Attention","created":"2024-03-21","cited_by_count":3,"area":null},{"id":"2412.15302","title":"Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification","created":"2024-12-19","cited_by_count":3,"area":null},{"id":"2306.11768","title":"Geometric Deep Learning for Structure-Based Drug Design: A Survey","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2310.09657","title":"Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation","created":"2023-10-14","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":"2410.11189","title":"Rethinking Graph Transformer Architecture Design for Node Classification","created":"2024-10-15","cited_by_count":2,"area":null},{"id":"2506.14098","title":"Toward a Graph Foundation Model: Pre-Training Transformers With Random Walks","created":"2025-06-17","cited_by_count":2,"area":null},{"id":"2508.00304","title":"Invariant Graph Transformer for Out-of-Distribution Generalization","created":"2025-08-01","cited_by_count":2,"area":null},{"id":"2405.01102","title":"Less is More: on the Over-Globalizing Problem in Graph Transformers","created":"2024-05-02","cited_by_count":1,"area":null},{"id":"2409.09007","title":"SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity","created":"2024-09-13","cited_by_count":1,"area":null},{"id":"2405.04114","title":"Acceleration Algorithms in GNNs: A Survey","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2406.00734","title":"GLADformer: A Mixed Perspective for Graph-level Anomaly Detection","created":"2024-06-02","cited_by_count":0,"area":null},{"id":"2407.09904","title":"Learning a Mini-batch Graph Transformer via Two-stage Interaction Augmentation","created":"2024-07-13","cited_by_count":0,"area":null},{"id":"2501.02760","title":"CHAT: Beyond Contrastive Graph Transformer for Link Prediction in Heterogeneous Networks","created":"2025-01-06","cited_by_count":0,"area":null},{"id":"2504.15075","title":"Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention","created":"2025-04-21","cited_by_count":0,"area":null},{"id":"2505.17660","title":"DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2508.00197","title":"Graph Lineages and Skeletal Graph Products","created":"2025-07-31","cited_by_count":0,"area":null},{"id":"2510.09767","title":"HeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network","created":"2025-10-10","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":"2601.17307","title":"Weighted Graph Clustering via Scale Contraction and Graph Structure Learning","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2604.27356","title":"TypeBandit: Type-Level Context Allocation and Reweighting for Effective Attribute Completion in Heterogeneous Graph Neural Networks","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2609.34420","title":"MegaGraph: Towards Efficient Training of Large-Scale Graph Transformers with Automated Hybrid Parallelism","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}