{"total":47,"rows":[{"id":"2305.16102","title":"Demystifying Oversmoothing in Attention-Based Graph Neural Networks","created":"2023-05-25","cited_by_count":49,"area":null},{"id":"2305.13084","title":"A Fractional Graph Laplacian Approach to Oversmoothing","created":"2023-05-22","cited_by_count":45,"area":null},{"id":"2306.01323","title":"Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?","created":"2023-06-02","cited_by_count":41,"area":null},{"id":"2309.17116","title":"Sheaf Hypergraph Networks","created":"2023-09-29","cited_by_count":34,"area":null},{"id":"2406.02997","title":"Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs","created":"2024-06-05","cited_by_count":34,"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":"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":"2307.01951","title":"A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks","created":"2023-07-04","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":"2401.09125","title":"Understanding Heterophily for Graph Neural Networks","created":"2024-01-17","cited_by_count":15,"area":null},{"id":"2306.07948","title":"Optimal Inference in Contextual Stochastic Block Models","created":"2023-06-06","cited_by_count":12,"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":"2312.04234","title":"Graph Convolutions Enrich the Self-Attention in Transformers!","created":"2023-12-07","cited_by_count":11,"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":"2405.20652","title":"Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs","created":"2024-05-31","cited_by_count":8,"area":null},{"id":"2306.01610","title":"Centered Self-Attention Layers","created":"2023-06-02","cited_by_count":7,"area":null},{"id":"2406.02269","title":"Graph Neural Networks Do Not Always Oversmooth","created":"2024-06-04","cited_by_count":7,"area":null},{"id":"2501.00762","title":"Residual connections provably mitigate oversmoothing in graph neural networks","created":"2025-01-01","cited_by_count":7,"area":null},{"id":"2502.04591","title":"Are We Measuring Oversmoothing in Graph Neural Networks Correctly?","created":"2025-02-07","cited_by_count":6,"area":null},{"id":"2306.11668","title":"Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations","created":"2023-06-20","cited_by_count":3,"area":null},{"id":"2410.04824","title":"Taming Gradient Oversmoothing and Expansion in Graph Neural Networks","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2410.14109","title":"Improving Graph Neural Networks by Learning Continuous Edge Directions","created":"2024-10-18","cited_by_count":3,"area":null},{"id":"2506.04653","title":"The Oversmoothing Fallacy: A Misguided Narrative in GNN Research","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2405.13987","title":"Analysis of Corrected Graph Convolutions","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2412.15496","title":"Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models","created":"2024-12-20","cited_by_count":2,"area":null},{"id":"2501.19089","title":"Resolving Oversmoothing with Opinion Dissensus","created":"2025-01-31","cited_by_count":2,"area":null},{"id":"2502.11394","title":"A Signed Graph Approach to Understanding and Mitigating Oversmoothing in GNNs","created":"2025-02-17","cited_by_count":2,"area":null},{"id":"2506.21338","title":"AGTCNet: A Graph-Temporal Approach for Principled Motor Imagery EEG Classification","created":"2025-06-26","cited_by_count":2,"area":null},{"id":"2603.17569","title":"Gaussian Process Limit Reveals Structural Benefits of Graph Transformers","created":"2026-03-18","cited_by_count":2,"area":null},{"id":"2312.10458","title":"Degree-based stratification of nodes in Graph Neural Networks","created":"2023-12-16","cited_by_count":1,"area":null},{"id":"2402.04033","title":"On provable privacy vulnerabilities of graph representations","created":"2024-02-06","cited_by_count":1,"area":null},{"id":"2407.19567","title":"Sharp Bounds for Poly-GNNs and the Effect of Graph Noise","created":"2024-07-28","cited_by_count":1,"area":null},{"id":"2409.06998","title":"Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow Variants","created":"2024-09-11","cited_by_count":1,"area":null},{"id":"2411.19517","title":"RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programs","created":"2024-11-29","cited_by_count":1,"area":null},{"id":"2312.04883","title":"Understanding Community Bias Amplification in Graph Representation Learning","created":"2023-12-08","cited_by_count":0,"area":null},{"id":"2404.10353","title":"Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis","created":"2024-04-16","cited_by_count":0,"area":null},{"id":"2410.14604","title":"Learning to Control the Smoothness of Graph Convolutional Network Features","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2412.13754","title":"Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks","created":"2024-12-18","cited_by_count":0,"area":null},{"id":"2505.19188","title":"Chordless Structure: A Pathway to Simple and Expressive GNNs","created":"2025-05-25","cited_by_count":0,"area":null},{"id":"2509.10337","title":"Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2512.01888","title":"Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets","created":"2025-12-01","cited_by_count":0,"area":null},{"id":"2602.12613","title":"Coden: Efficient Temporal Graph Neural Networks for Continuous Prediction","created":"2026-02-13","cited_by_count":0,"area":null},{"id":"2603.09257","title":"Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2604.24293","title":"Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2607.16676","title":"The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy","created":"2026-07-18","cited_by_count":0,"area":null},{"id":"2608.12514","title":"Exploring Oversmoothing with Householder Matrices","created":"2026-08-12","cited_by_count":0,"area":null}],"next_cursor":null}