{"total":64,"rows":[{"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":"2404.04612","title":"Spectral Graph Pruning Against Over-Squashing and Over-Smoothing","created":"2024-04-06","cited_by_count":24,"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":"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":"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":"2305.09703","title":"Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting","created":"2023-05-16","cited_by_count":16,"area":null},{"id":"2407.01214","title":"Revisiting Random Walks for Learning on Graphs","created":"2024-07-01","cited_by_count":13,"area":null},{"id":"2502.04891","title":"GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring","created":"2025-02-07","cited_by_count":13,"area":null},{"id":"2408.07191","title":"Joint Graph Rewiring and Feature Denoising via Spectral Resonance","created":"2024-08-13","cited_by_count":12,"area":null},{"id":"2407.09381","title":"The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited","created":"2024-07-12","cited_by_count":11,"area":null},{"id":"2406.03671","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","created":"2024-06-06","cited_by_count":10,"area":null},{"id":"2408.03669","title":"Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks","created":"2024-08-07","cited_by_count":10,"area":null},{"id":"2405.18968","title":"UniIF: Unified Molecule Inverse Folding","created":"2024-05-29","cited_by_count":9,"area":null},{"id":"2410.22377","title":"A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification","created":"2024-10-29","cited_by_count":9,"area":null},{"id":"2405.18877","title":"Continuous Product Graph Neural Networks","created":"2024-05-29","cited_by_count":8,"area":null},{"id":"2410.00807","title":"WiGNet: Windowed Vision Graph Neural Network","created":"2024-10-01","cited_by_count":8,"area":null},{"id":"2411.17429","title":"Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey","created":"2024-11-26","cited_by_count":8,"area":null},{"id":"2503.01079","title":"Depth-Adaptive Graph Neural Networks via Learnable Bakry-'Emery Curvature","created":"2025-03-03","cited_by_count":8,"area":null},{"id":"2401.14580","title":"Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks","created":"2024-01-26","cited_by_count":6,"area":null},{"id":"2310.04190","title":"On the Two Sides of Redundancy in Graph Neural Networks","created":"2023-10-06","cited_by_count":4,"area":null},{"id":"2406.12841","title":"Demystifying Higher-Order Graph Neural Networks","created":"2024-06-18","cited_by_count":4,"area":null},{"id":"2411.02279","title":"Enhancing the Influence of Labels on Unlabeled Nodes in Graph Convolutional Networks","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2508.19071","title":"Dynamic Triangulation-Based Graph Rewiring for Graph Neural Networks","created":"2025-08-26","cited_by_count":4,"area":null},{"id":"2601.02451","title":"mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks","created":"2026-01-05","cited_by_count":4,"area":null},{"id":"2403.00720","title":"Subhomogeneous Deep Equilibrium Models","created":"2024-03-01","cited_by_count":3,"area":null},{"id":"2407.20067","title":"xAI-Drop: Don't Use What You Cannot Explain","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2410.05593","title":"When Graph Neural Networks Meet Dynamic Mode Decomposition","created":"2024-10-08","cited_by_count":3,"area":null},{"id":"2509.01170","title":"ADMP-GNN: Adaptive Depth Message Passing GNN","created":"2025-09-01","cited_by_count":3,"area":null},{"id":"2403.07943","title":"Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack","created":"2024-03-10","cited_by_count":2,"area":null},{"id":"2408.03152","title":"TSC: A Simple Two-Sided Constraint against Over-Smoothing","created":"2024-08-06","cited_by_count":2,"area":null},{"id":"2503.12919","title":"Continuous Simplicial Neural Networks","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2504.15920","title":"ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion","created":"2025-04-22","cited_by_count":2,"area":null},{"id":"2509.01254","title":"What Expressivity Theory Misses: Message Passing Complexity for GNNs","created":"2025-09-01","cited_by_count":2,"area":null},{"id":"2405.18879","title":"Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks","created":"2024-05-29","cited_by_count":1,"area":null},{"id":"2411.07150","title":"Variational Graph Contrastive Learning","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2502.07364","title":"Effects of Dropout on Performance in Long-range Graph Learning Tasks","created":"2025-02-11","cited_by_count":1,"area":null},{"id":"2502.20885","title":"A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning","created":"2025-02-28","cited_by_count":1,"area":null},{"id":"2508.05070","title":"TANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows","created":"2025-08-07","cited_by_count":1,"area":null},{"id":"2508.19352","title":"Memorization in Graph Neural Networks","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.24115","title":"ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs","created":"2025-09-28","cited_by_count":1,"area":null},{"id":"2510.26533","title":"Higher-Order Regularization Learning on Hypergraphs","created":"2025-10-30","cited_by_count":1,"area":null},{"id":"2512.06782","title":"Measuring Over-smoothing beyond Dirichlet energy","created":"2025-12-07","cited_by_count":1,"area":null},{"id":"2603.11944","title":"Effective Resistance Rewiring: A Simple Topological Correction for Over-Squashing","created":"2026-03-12","cited_by_count":1,"area":null},{"id":"2306.04828","title":"Fast and Effective GNN Training through Sequences of Random Path Graphs","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2309.17335","title":"Asynchronous Graph Generator","created":"2023-09-29","cited_by_count":0,"area":null},{"id":"2405.14742","title":"HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning","created":"2024-05-23","cited_by_count":0,"area":null},{"id":"2407.14387","title":"GLAudio Listens to the Sound of the Graph","created":"2024-07-19","cited_by_count":0,"area":null},{"id":"2410.08759","title":"Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis","created":"2024-10-11","cited_by_count":0,"area":null},{"id":"2507.21422","title":"GraphTorque: Torque-Driven Rewiring Graph Neural Network","created":"2025-07-29","cited_by_count":0,"area":null},{"id":"2509.02015","title":"Second-Order Tensorial Partial Differential Equations on Graphs","created":"2025-09-02","cited_by_count":0,"area":null},{"id":"2509.06777","title":"Asynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2510.25892","title":"Topology-Aware Active Learning on Graphs","created":"2025-10-29","cited_by_count":0,"area":null},{"id":"2512.15600","title":"How Smoothing is N-simplicial Attention?","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2602.21092","title":"Probing Graph Neural Network Activation Patterns Through Graph Topology","created":"2026-02-24","cited_by_count":0,"area":null},{"id":"2603.26178","title":"Geometric Evolution Graph Convolutional Networks: Enhancing Graph Representation Learning via Ricci Flow","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2605.00951","title":"Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2605.08074","title":"GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.10975","title":"Hierarchical Multi-Scale Graph Neural Networks: Scalable Heterophilous Learning with Oversmoothing and Oversquashing Mitigation","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.13383","title":"Beyond Oversquashing: Understanding Signal Propagation in GNNs Via Observables","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2606.19185","title":"AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.21333","title":"Ramanujan Graph Rewiring with Non Negative Resistance Curvature","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2608.09596","title":"LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2609.32362","title":"StegGNN: Learning Graphical Representation for Image Steganography","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2610.04157","title":"Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective","created":"2026-10-03","cited_by_count":0,"area":null}],"next_cursor":null}