{"total":22,"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":"2410.03424","title":"Cayley Graph Propagation","created":"2024-10-04","cited_by_count":21,"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":"2406.03671","title":"PANDA: Expanded Width-Aware Message Passing Beyond Rewiring","created":"2024-06-06","cited_by_count":10,"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":"2504.04052","title":"PIORF: Physics-Informed Ollivier-Ricci Flow for Long-Range Interactions in Mesh Graph Neural Networks","created":"2025-04-05","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":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","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":"2508.09427","title":"Implicit Hypergraph Neural Networks: A Stable Framework for Higher-Order Relational Learning with Provable Guarantees","created":"2025-08-13","cited_by_count":3,"area":null},{"id":"2511.13010","title":"Are Graph Transformers Necessary? Efficient Long-Range Message Passing with Fractal Nodes in MPNNs","created":"2025-11-17","cited_by_count":3,"area":null},{"id":"2506.06582","title":"Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2410.05697","title":"Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning","created":"2024-10-08","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":"2509.12530","title":"Graph Homophily Booster: Rethinking the Role of Discrete Features on Heterophilic Graphs","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2511.12709","title":"Adaptive Graph Rewiring to Mitigate Over-Squashing in Mesh-Based GNNs for Fluid Dynamics Simulations","created":"2025-11-16","cited_by_count":1,"area":null},{"id":"2602.07256","title":"Graph homophily booster: Reimagining the role of discrete features in heterophilic graph learning","created":"2026-02-06","cited_by_count":1,"area":null},{"id":"2410.10464","title":"Information propagation dynamics in Deep Graph Networks","created":"2024-10-14","cited_by_count":0,"area":null},{"id":"2502.06126","title":"Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2505.10392","title":"Schreier-Coset Graph Propagation","created":"2025-05-15","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}],"next_cursor":null}