{"total":462,"rows":[{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713,"area":null},{"id":"2303.10993","title":"A Survey on Oversmoothing in Graph Neural Networks","created":"2023-03-20","cited_by_count":247,"area":null},{"id":"2312.02783","title":"Large Language Models on Graphs: A Comprehensive Survey","created":"2023-12-05","cited_by_count":192,"area":null},{"id":"2303.06147","title":"Exphormer: Sparse Transformers for Graphs","created":"2023-03-10","cited_by_count":170,"area":null},{"id":"2302.02941","title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology","created":"2023-02-06","cited_by_count":166,"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":"2202.08235","title":"Data Augmentation for Deep Graph Learning: A Survey","created":"2022-02-16","cited_by_count":133,"area":null},{"id":"2202.02296","title":"Graph-Coupled Oscillator Networks","created":"2022-02-04","cited_by_count":124,"area":null},{"id":"2301.09505","title":"Rethinking the Expressive Power of GNNs via Graph Biconnectivity","created":"2023-01-23","cited_by_count":119,"area":null},{"id":"2210.11790","title":"FoSR: First-order spectral rewiring for addressing oversquashing in GNNs","created":"2022-10-21","cited_by_count":105,"area":null},{"id":"2303.01028","title":"Specformer: Spectral Graph Neural Networks Meet Transformers","created":"2023-03-02","cited_by_count":102,"area":null},{"id":"2211.15779","title":"Revisiting Over-smoothing and Over-squashing Using Ollivier-Ricci Curvature","created":"2022-11-28","cited_by_count":98,"area":null},{"id":"2302.06835","title":"Understanding Oversquashing in GNNs through the Lens of Effective Resistance","created":"2023-02-14","cited_by_count":97,"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":"2406.04267","title":"Transformers need glasses! Information over-squashing in language tasks","created":"2024-06-06","cited_by_count":91,"area":null},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90,"area":null},{"id":"2305.10498","title":"Edge Directionality Improves Learning on Heterophilic Graphs","created":"2023-05-17","cited_by_count":90,"area":null},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83,"area":null},{"id":"2305.08018","title":"DRew: Dynamically Rewired Message Passing with Delay","created":"2023-05-13","cited_by_count":82,"area":null},{"id":"2209.15486","title":"Graph Neural Networks for Link Prediction with Subgraph Sketching","created":"2022-09-30","cited_by_count":81,"area":null},{"id":"2206.07369","title":"DiffWire: Inductive Graph Rewiring via the Lov\\'asz Bound","created":"2022-06-15","cited_by_count":79,"area":null},{"id":"2210.02997","title":"Expander Graph Propagation","created":"2022-10-06","cited_by_count":75,"area":null},{"id":"2302.01524","title":"Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing","created":"2023-02-03","cited_by_count":67,"area":null},{"id":"2204.01855","title":"A Survey on Graph Representation Learning Methods","created":"2022-04-04","cited_by_count":66,"area":null},{"id":"2402.08871","title":"Position: Topological Deep Learning is the New Frontier for Relational Learning","created":"2024-02-14","cited_by_count":66,"area":null},{"id":"2212.02374","title":"On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks","created":"2022-12-05","cited_by_count":64,"area":null},{"id":"2202.08871","title":"Graph Data Augmentation for Graph Machine Learning: A Survey","created":"2022-02-17","cited_by_count":62,"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":"2302.01018","title":"Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities","created":"2023-02-02","cited_by_count":61,"area":null},{"id":"2210.09789","title":"Anti-Symmetric DGN: a stable architecture for Deep Graph Networks","created":"2022-10-18","cited_by_count":60,"area":null},{"id":"2206.10991","title":"Understanding convolution on graphs via energies","created":"2022-06-22","cited_by_count":59,"area":null},{"id":"2310.01668","title":"Locality-Aware Graph-Rewiring in GNNs","created":"2023-10-02","cited_by_count":56,"area":null},{"id":"2208.03471","title":"Oversquashing in GNNs through the lens of information contraction and graph expansion","created":"2022-08-06","cited_by_count":53,"area":null},{"id":"2306.03589","title":"How does over-squashing affect the power of GNNs?","created":"2023-06-06","cited_by_count":53,"area":null},{"id":"2210.00513","title":"Gradient Gating for Deep Multi-Rate Learning on Graphs","created":"2022-10-02","cited_by_count":49,"area":null},{"id":"2202.11097","title":"Message passing all the way up","created":"2022-02-22","cited_by_count":47,"area":null},{"id":"2206.01003","title":"Shortest Path Networks for Graph Property Prediction","created":"2022-06-02","cited_by_count":46,"area":null},{"id":"2502.10818","title":"On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning","created":"2025-02-15","cited_by_count":43,"area":null},{"id":"2502.17598","title":"Hallucination Detection in LLMs Using Spectral Features of Attention Maps","created":"2025-02-24","cited_by_count":43,"area":null},{"id":"2309.09384","title":"Mitigating Over-Smoothing and Over-Squashing using Augmentations of Forman-Ricci Curvature","created":"2023-09-17","cited_by_count":41,"area":null},{"id":"2202.13852","title":"Hyperbolic Graph Neural Networks: A Review of Methods and Applications","created":"2022-02-28","cited_by_count":40,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2203.13913","title":"SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks","created":"2022-03-25","cited_by_count":37,"area":null},{"id":"2201.12674","title":"Rewiring with Positional Encodings for Graph Neural Networks","created":"2022-01-29","cited_by_count":36,"area":null},{"id":"2310.01267","title":"Cooperative Graph Neural Networks","created":"2023-10-02","cited_by_count":36,"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":"2307.07107","title":"Graph Positional and Structural Encoder","created":"2023-07-14","cited_by_count":33,"area":null},{"id":"2208.08302","title":"Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing","created":"2022-08-17","cited_by_count":31,"area":null},{"id":"2209.07754","title":"On the Robustness of Graph Neural Diffusion to Topology Perturbations","created":"2022-09-16","cited_by_count":30,"area":null},{"id":"2202.01606","title":"Graph Coloring with Physics-Inspired Graph Neural Networks","created":"2022-02-03","cited_by_count":29,"area":null},{"id":"2305.02931","title":"Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering","created":"2023-05-03","cited_by_count":29,"area":null},{"id":"2405.01009","title":"On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems","created":"2024-05-02","cited_by_count":28,"area":null},{"id":"2308.08235","title":"The Expressive Power of Graph Neural Networks: A Survey","created":"2023-08-16","cited_by_count":27,"area":null},{"id":"2306.02866","title":"Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance","created":"2023-06-05","cited_by_count":26,"area":null},{"id":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26,"area":null},{"id":"2310.10121","title":"From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond","created":"2023-10-16","cited_by_count":26,"area":null},{"id":"2407.11095","title":"DeepGate3: Towards Scalable Circuit Representation Learning","created":"2024-07-15","cited_by_count":26,"area":null},{"id":"2306.02376","title":"Towards Deep Attention in Graph Neural Networks: Problems and Remedies","created":"2023-06-04","cited_by_count":24,"area":null},{"id":"2312.16560","title":"Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching","created":"2023-12-27","cited_by_count":24,"area":null},{"id":"2404.04612","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","created":"2024-04-06","cited_by_count":24,"area":null},{"id":"2405.12474","title":"How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing","created":"2024-05-21","cited_by_count":24,"area":null},{"id":"2202.01185","title":"Heterogeneous manifolds for curvature-aware graph embedding","created":"2022-02-02","cited_by_count":23,"area":null},{"id":"2209.08264","title":"Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach","created":"2022-09-17","cited_by_count":23,"area":null},{"id":"2210.13148","title":"Transformers over Directed Acyclic Graphs","created":"2022-10-24","cited_by_count":23,"area":null},{"id":"2211.16199","title":"Latent Graph Inference using Product Manifolds","created":"2022-11-26","cited_by_count":23,"area":null},{"id":"2304.05059","title":"Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification","created":"2023-04-11","cited_by_count":23,"area":null},{"id":"2308.15568","title":"Over-Squashing in Graph Neural Networks: A Comprehensive survey","created":"2023-08-29","cited_by_count":23,"area":null},{"id":"2401.01232","title":"Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learning","created":"2024-01-02","cited_by_count":23,"area":null},{"id":"2303.11323","title":"Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back","created":"2023-03-20","cited_by_count":22,"area":null},{"id":"2311.07073","title":"Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges","created":"2023-11-13","cited_by_count":22,"area":null},{"id":"2402.11565","title":"Continual Learning on Graphs: Challenges, Solutions, and Opportunities","created":"2024-02-18","cited_by_count":21,"area":null},{"id":"2410.03424","title":"Cayley Graph Propagation","created":"2024-10-04","cited_by_count":21,"area":null},{"id":"2210.09521","title":"A Practical, Progressively-Expressive GNN","created":"2022-10-18","cited_by_count":20,"area":null},{"id":"2306.11443","title":"UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction","created":"2023-06-20","cited_by_count":20,"area":null},{"id":"2308.14522","title":"Graph Meets LLMs: Towards Large Graph Models","created":"2023-08-28","cited_by_count":20,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2407.09468","title":"Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures","created":"2024-07-12","cited_by_count":20,"area":null},{"id":"2503.09008","title":"Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement","created":"2025-03-12","cited_by_count":20,"area":null},{"id":"2207.11311","title":"Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective","created":"2022-07-22","cited_by_count":19,"area":null},{"id":"2301.12906","title":"Curvature Filtrations for Graph Generative Model Evaluation","created":"2023-01-30","cited_by_count":19,"area":null},{"id":"2305.16174","title":"From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module","created":"2023-05-25","cited_by_count":19,"area":null},{"id":"2505.15547","title":"Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning","created":"2025-05-21","cited_by_count":19,"area":null},{"id":"2302.06299","title":"Homophily-oriented Heterogeneous Graph Rewiring","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2307.08390","title":"Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection","created":"2023-07-17","cited_by_count":18,"area":null},{"id":"2309.04810","title":"Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization","created":"2023-09-09","cited_by_count":18,"area":null},{"id":"2312.08515","title":"Simplicial Representation Learning with Neural $k$-Forms","created":"2023-12-13","cited_by_count":18,"area":null},{"id":"2405.19121","title":"Spatio-Spectral Graph Neural Networks","created":"2024-05-29","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":"2401.12780","title":"DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing","created":"2024-01-23","cited_by_count":16,"area":null},{"id":"2201.09332","title":"How Expressive are Transformers in Spectral Domain for Graphs?","created":"2022-01-23","cited_by_count":15,"area":null},{"id":"2201.13410","title":"Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings","created":"2022-01-31","cited_by_count":15,"area":null},{"id":"2304.05729","title":"Dynamic Graph Representation Learning with Neural Networks: A Survey","created":"2023-04-12","cited_by_count":15,"area":null},{"id":"2309.04082","title":"Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning","created":"2023-09-08","cited_by_count":15,"area":null},{"id":"2404.07194","title":"VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification","created":"2024-04-10","cited_by_count":15,"area":null},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2207.08026","title":"Rewiring Networks for Graph Neural Network Training Using Discrete Geometry","created":"2022-07-16","cited_by_count":14,"area":null},{"id":"2212.01793","title":"kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning","created":"2022-12-04","cited_by_count":14,"area":null},{"id":"2212.06538","title":"Leave Graphs Alone: Addressing Over-Squashing without Rewiring","created":"2022-12-13","cited_by_count":14,"area":null},{"id":"2405.13526","title":"Understanding Virtual Nodes: Oversquashing and Node Heterogeneity","created":"2024-05-22","cited_by_count":14,"area":null}],"next_cursor":"100"}