{"total":58,"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":"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":"2406.02740","title":"Long Range Propagation on Continuous-Time Dynamic Graphs","created":"2024-06-04","cited_by_count":28,"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":"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":"2308.15568","title":"Over-Squashing in Graph Neural Networks: A Comprehensive survey","created":"2023-08-29","cited_by_count":23,"area":null},{"id":"2307.06104","title":"Deep learning for dynamic graphs: models and benchmarks","created":"2023-07-12","cited_by_count":19,"area":null},{"id":"2404.17099","title":"Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND","created":"2024-04-26","cited_by_count":17,"area":null},{"id":"2307.16092","title":"Feature Transportation Improves Graph Neural Networks","created":"2023-07-29","cited_by_count":15,"area":null},{"id":"2503.23167","title":"Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks","created":"2025-03-29","cited_by_count":14,"area":null},{"id":"2506.07624","title":"Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks","created":"2025-06-09","cited_by_count":12,"area":null},{"id":"2404.19508","title":"Temporal Graph ODEs for Irregularly-Sampled Time Series","created":"2024-04-30","cited_by_count":11,"area":null},{"id":"2502.02719","title":"Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective","created":"2025-02-04","cited_by_count":11,"area":null},{"id":"2505.18728","title":"Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling","created":"2025-05-24","cited_by_count":10,"area":null},{"id":"2512.17762","title":"Can You Hear Me Now? A Benchmark for Long-Range Graph Propagation","created":"2025-12-19","cited_by_count":10,"area":null},{"id":"2401.11074","title":"On The Temporal Domain of Differential Equation Inspired Graph Neural Networks","created":"2024-01-20","cited_by_count":9,"area":null},{"id":"2402.04621","title":"Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective","created":"2024-02-07","cited_by_count":9,"area":null},{"id":"2402.05027","title":"Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing","created":"2024-02-07","cited_by_count":8,"area":null},{"id":"2503.16207","title":"Neural Variable-Order Fractional Differential Equation Networks","created":"2025-03-20","cited_by_count":6,"area":null},{"id":"2506.15507","title":"Over-squashing in Spatiotemporal Graph Neural Networks","created":"2025-06-18","cited_by_count":6,"area":null},{"id":"2511.08185","title":"Improving Long-Range Interactions in Graph Neural Simulators via Hamiltonian Dynamics","created":"2025-11-11","cited_by_count":6,"area":null},{"id":"2308.09570","title":"Investigating the Interplay between Features and Structures in Graph Learning","created":"2023-08-18","cited_by_count":5,"area":null},{"id":"2310.01704","title":"Transformers are efficient hierarchical chemical graph learners","created":"2023-10-02","cited_by_count":4,"area":null},{"id":"2402.08653","title":"SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2503.16666","title":"Efficient Training of Neural Fractional-Order Differential Equation via Adjoint Backpropagation","created":"2025-03-20","cited_by_count":3,"area":null},{"id":"2604.10955","title":"Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing","created":"2026-04-13","cited_by_count":3,"area":null},{"id":"2404.11276","title":"Towards Data-Centric Automatic R&D","created":"2024-04-17","cited_by_count":2,"area":null},{"id":"2405.14286","title":"Modeling Edge-Specific Node Features through Co-Representation Neural Hypergraph Diffusion","created":"2024-05-23","cited_by_count":2,"area":null},{"id":"2407.18480","title":"Scalable Graph Compressed Convolutions","created":"2024-07-26","cited_by_count":2,"area":null},{"id":"2505.18088","title":"Early-Exit Graph Neural Networks","created":"2025-05-23","cited_by_count":2,"area":null},{"id":"2508.00127","title":"Structured Transformations for Stable and Interpretable Neural Computation","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2302.08727","title":"Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks","created":"2023-02-17","cited_by_count":1,"area":null},{"id":"2402.14802","title":"Link Prediction with Physics-Inspired Graph Neural Networks","created":"2024-02-22","cited_by_count":1,"area":null},{"id":"2405.11968","title":"Conditional Shift-Robust Conformal Prediction for Graph Neural Network","created":"2024-05-20","cited_by_count":1,"area":null},{"id":"2508.02126","title":"Understanding Learning Dynamics Through Structured Representations","created":"2025-08-04","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":"2510.08450","title":"gLSTM: Mitigating Over-Squashing by Increasing Storage Capacity","created":"2025-10-09","cited_by_count":1,"area":null},{"id":"2605.12358","title":"From Message-Passing to Linearized Graph Sequence Models","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2405.01247","title":"Lying Graph Convolution: Learning to Lie for Node Classification Tasks","created":"2024-05-02","cited_by_count":0,"area":null},{"id":"2407.11358","title":"SES: Bridging the Gap Between Explainability and Prediction of Graph Neural Networks","created":"2024-07-16","cited_by_count":0,"area":null},{"id":"2407.11361","title":"Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks","created":"2024-07-16","cited_by_count":0,"area":null},{"id":"2410.10464","title":"Information propagation dynamics in Deep Graph Networks","created":"2024-10-14","cited_by_count":0,"area":null},{"id":"2503.11786","title":"Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensors","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2508.14097","title":"Non-Dissipative Graph Propagation for Non-Local Community Detection","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2512.23137","title":"Graph Neural Networks with Transformer Fusion of Brain Connectivity Dynamics and Tabular Data for Forecasting Future Tobacco Use","created":"2025-12-29","cited_by_count":0,"area":null},{"id":"2602.18141","title":"Geometry-Induced Diffusion on Graphs: A Learnable Weighted Laplacian for Spectral GNNs","created":"2026-02-20","cited_by_count":0,"area":null},{"id":"2605.18387","title":"Graph Hierarchical Recurrence for Long-Range Generalization","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2605.23467","title":"S$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2606.20747","title":"CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.10804","title":"When does distribution shift break graph neural networks calibration?","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.24319","title":"Can a Dynamic Internal Field Govern a Transformer's Cognition? Certifiability, not Superiority, in Homeostatic Compute Control","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2609.14977","title":"LiftGCN: Efficient Energy-Preserving Graph Learning via Joukowski Spectral Lifting for Finite Element Stress Prediction","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2609.32940","title":"TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks","created":"2026-09-26","cited_by_count":0,"area":null},{"id":"2609.33674","title":"Reachability is not enough: Diagnosing long-range behavior in GNNs","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.39739","title":"Stable Transformers for Graph Generation","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.03556","title":"Get a GRIP, this will be a long TRIP: A Quantifiable Long-Range Framework for Verifying Over-squashing","created":"2026-10-02","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}