{"total":105,"rows":[{"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":"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":"2212.02374","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":"2310.01668","title":"Locality-Aware Graph-Rewiring in GNNs","created":"2023-10-02","cited_by_count":56,"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":"2502.14546","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","created":"2025-02-20","cited_by_count":53,"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":"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":"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":"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":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26,"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":"2308.15568","title":"Over-Squashing in Graph Neural Networks: A Comprehensive survey","created":"2023-08-29","cited_by_count":23,"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":"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":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2311.14864","title":"Effective Structural Encodings via Local Curvature Profiles","created":"2023-11-24","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":"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":"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":"2506.05971","title":"On Measuring Long-Range Interactions in Graph Neural Networks","created":"2025-06-06","cited_by_count":11,"area":null},{"id":"2402.01242","title":"Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness","created":"2024-02-02","cited_by_count":10,"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":"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":"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":"2506.16110","title":"Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification","created":"2025-06-19","cited_by_count":8,"area":null},{"id":"2403.11408","title":"Layer-diverse Negative Sampling for Graph Neural Networks","created":"2024-03-18","cited_by_count":7,"area":null},{"id":"2510.17457","title":"Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models","created":"2025-10-20","cited_by_count":7,"area":null},{"id":"2312.04762","title":"The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure","created":"2023-12-08","cited_by_count":6,"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":"2410.15059","title":"Deep Equilibrium Algorithmic Reasoning","created":"2024-10-19","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":"2506.15507","title":"Over-squashing in Spatiotemporal Graph Neural Networks","created":"2025-06-18","cited_by_count":6,"area":null},{"id":"2403.11199","title":"Graph Unitary Message Passing","created":"2024-03-17","cited_by_count":5,"area":null},{"id":"2410.20516","title":"A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing","created":"2024-10-27","cited_by_count":5,"area":null},{"id":"2311.07966","title":"Higher-Order Expander Graph Propagation","created":"2023-11-14","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":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2506.04694","title":"Influence Functions for Edge Edits in Non-Convex Graph Neural Networks","created":"2025-06-05","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":"2511.20406","title":"Short-Range Oversquashing","created":"2025-11-25","cited_by_count":3,"area":null},{"id":"2405.16185","title":"Differentiable Cluster Graph Neural Network","created":"2024-05-25","cited_by_count":2,"area":null},{"id":"2407.05649","title":"Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention","created":"2024-07-08","cited_by_count":2,"area":null},{"id":"2503.00547","title":"Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing","created":"2025-03-01","cited_by_count":2,"area":null},{"id":"2506.01728","title":"Principled Data Augmentation for Learning to Solve Quadratic Programming Problems","created":"2025-06-02","cited_by_count":2,"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":"2601.07419","title":"Position: Don't be Afraid of Over-Smoothing And Over-Squashing","created":"2026-01-12","cited_by_count":2,"area":null},{"id":"2602.15634","title":"Beyond ReLU: Bifurcation, Oversmoothing, and Topological Priors","created":"2026-02-17","cited_by_count":2,"area":null},{"id":"2311.15945","title":"Over-Squashing in Riemannian Graph Neural Networks","created":"2023-11-27","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":"2410.05536","title":"Accelerating Flood Warnings by 10 Hours: The Power of River Network Topology in AI-enhanced Flood Forecasting","created":"2024-10-07","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":"2508.09265","title":"Over-Squashing in GNNs and Causal Inference of Rewiring Strategies","created":"2025-08-12","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":"2508.20597","title":"Local Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks","created":"2025-08-28","cited_by_count":1,"area":null},{"id":"2510.20556","title":"Structural Invariance Matters: Rethinking Graph Rewiring through Graph Metrics","created":"2025-10-23","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":"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":"2512.06782","title":"Measuring Over-smoothing beyond Dirichlet energy","created":"2025-12-07","cited_by_count":1,"area":null},{"id":"2512.09182","title":"Understanding the Failure Modes of Transformers through the Lens of Graph Neural Networks","created":"2025-12-09","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":"2307.05217","title":"Supervised Attention Using Homophily in Graph Neural Networks","created":"2023-07-11","cited_by_count":0,"area":null},{"id":"2311.18177","title":"An Effective Universal Polynomial Basis for Spectral Graph Neural Networks","created":"2023-11-30","cited_by_count":0,"area":null},{"id":"2402.06908","title":"Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions","created":"2024-02-10","cited_by_count":0,"area":null},{"id":"2411.12052","title":"HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling","created":"2024-11-18","cited_by_count":0,"area":null},{"id":"2501.06002","title":"DeltaGNN: Graph Neural Network with Information Flow Control","created":"2025-01-10","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":"2505.12411","title":"It Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph","created":"2025-05-18","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":"2508.07659","title":"Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning","created":"2025-08-11","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":"2509.13139","title":"Learning from Heterophilic Graphs: A Spectral Theory Perspective on the Impact of Self-Loops and Parallel Edges","created":"2025-09-16","cited_by_count":0,"area":null},{"id":"2510.22599","title":"A roadmap for curvature-based geometric data analysis and learning","created":"2025-10-26","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":"2601.14522","title":"On the Runway Cascade of Transformers for Language Modeling","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2601.18917","title":"GraIP: A Benchmarking Framework For Neural Graph Inverse Problems","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2602.10754","title":"Exploring the impact of adaptive rewiring in Graph Neural Networks","created":"2026-02-11","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":"2603.01388","title":"Invariant-Stratified Propagation for Expressive Graph Neural Networks","created":"2026-03-02","cited_by_count":0,"area":null},{"id":"2603.27529","title":"Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs","created":"2026-03-29","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":"2605.07577","title":"Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2605.09457","title":"RAwR: Role-Aware Rewiring via Approximate Equitable Partition","created":"2026-05-10","cited_by_count":0,"area":null},{"id":"2605.13126","title":"MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing","created":"2026-05-13","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":"2606.00757","title":"RADE: Random Add-Drop Edge as a Regularizer","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.03068","title":"Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.05046","title":"Graph Cascades: Contagion-Based Mesoscopic Rewiring for Structure-Aware Graph Machine Learning","created":"2026-06-03","cited_by_count":0,"area":null},{"id":"2606.17684","title":"Geometrical fairness in graph neural networks","created":"2026-06-16","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":"2607.21607","title":"Spectral Flow Certificates for Depth-Aware Long-Range Propagation in Graph Neural Networks","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2607.22381","title":"Local-Global Geometric Insights for Graph Neural Networks via Entropic Curvature","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2607.27479","title":"Schreier-Coset Graph Rewiring","created":"2026-07-29","cited_by_count":0,"area":null}],"next_cursor":"100"}