{"total":51,"rows":[{"id":"2202.07987","title":"Out-Of-Distribution Generalization on Graphs: A Survey","created":"2022-02-16","cited_by_count":97,"area":null},{"id":"2411.06070","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","created":"2024-11-09","cited_by_count":85,"area":null},{"id":"2305.08048","title":"Towards Understanding the Generalization of Graph Neural Networks","created":"2023-05-14","cited_by_count":41,"area":null},{"id":"2302.04451","title":"Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion","created":"2023-02-09","cited_by_count":40,"area":null},{"id":"2412.16441","title":"Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees","created":"2024-12-21","cited_by_count":32,"area":null},{"id":"2301.11039","title":"WL meet VC","created":"2023-01-26","cited_by_count":31,"area":null},{"id":"2406.05225","title":"A Manifold Perspective on the Statistical Generalization of Graph Neural Networks","created":"2024-06-07","cited_by_count":17,"area":null},{"id":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16,"area":null},{"id":"2406.01977","title":"What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding","created":"2024-06-04","cited_by_count":14,"area":null},{"id":"2212.13069","title":"Homophily modulates double descent generalization in graph convolution networks","created":"2022-12-26","cited_by_count":13,"area":null},{"id":"2402.07568","title":"Weisfeiler-Leman at the margin: When more expressivity matters","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2410.10051","title":"Towards Bridging Generalization and Expressivity of Graph Neural Networks","created":"2024-10-14","cited_by_count":12,"area":null},{"id":"2210.09809","title":"Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel","created":"2022-10-18","cited_by_count":8,"area":null},{"id":"2401.12362","title":"VC dimension of Graph Neural Networks with Pfaffian activation functions","created":"2024-01-22","cited_by_count":6,"area":null},{"id":"2409.05191","title":"Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions","created":"2024-09-08","cited_by_count":6,"area":null},{"id":"2410.08473","title":"Deeper Insights into Deep Graph Convolutional Networks: Stability and Generalization","created":"2024-10-11","cited_by_count":6,"area":null},{"id":"2402.04038","title":"PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network","created":"2024-02-06","cited_by_count":4,"area":null},{"id":"2402.07025","title":"Generalization Error of Graph Neural Networks in the Mean-field Regime","created":"2024-02-10","cited_by_count":4,"area":null},{"id":"2408.13878","title":"Generalization of Graph Neural Networks is Robust to Model Mismatch","created":"2024-08-25","cited_by_count":4,"area":null},{"id":"2402.03818","title":"Asymptotic generalization error of a single-layer graph convolutional network","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2403.04482","title":"On the Topology Awareness and Generalization Performance of Graph Neural Networks","created":"2024-03-07","cited_by_count":3,"area":null},{"id":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2307.02126","title":"Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix","created":"2023-07-05","cited_by_count":2,"area":null},{"id":"2311.04561","title":"Information-Theoretic Generalization Bounds for Transductive Learning and its Applications","created":"2023-11-08","cited_by_count":2,"area":null},{"id":"2402.02692","title":"Statistical Guarantees for Link Prediction using Graph Neural Networks","created":"2024-02-05","cited_by_count":2,"area":null},{"id":"2405.06418","title":"PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning","created":"2024-05-10","cited_by_count":2,"area":null},{"id":"2406.01249","title":"Equivariant Machine Learning on Graphs with Nonlinear Spectral Filters","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2411.08638","title":"Graph Neural Network Generalization with Gaussian Mixture Model Based Augmentation","created":"2024-11-13","cited_by_count":2,"area":null},{"id":"2501.12554","title":"Generalization Performance of Hypergraph Neural Networks","created":"2025-01-22","cited_by_count":2,"area":null},{"id":"2503.01361","title":"Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2507.00927","title":"Understanding Generalization in Node and Link Prediction","created":"2025-07-01","cited_by_count":2,"area":null},{"id":"2309.13459","title":"A Model-Agnostic Graph Neural Network for Integrating Local and Global Information","created":"2023-09-23","cited_by_count":1,"area":null},{"id":"2403.06079","title":"Generalization of Graph Neural Networks through the Lens of Homomorphism","created":"2024-03-10","cited_by_count":1,"area":null},{"id":"2502.12937","title":"Tuning Algorithmic and Architectural Hyperparameters in Graph-Based Semi-Supervised Learning with Provable Guarantees","created":"2025-02-18","cited_by_count":1,"area":null},{"id":"2509.07499","title":"Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks","created":"2025-09-09","cited_by_count":1,"area":null},{"id":"2604.10553","title":"Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks","created":"2026-04-12","cited_by_count":1,"area":null},{"id":"2210.14843","title":"TuneUp: A Simple Improved Training Strategy for Graph Neural Networks","created":"2022-10-26","cited_by_count":0,"area":null},{"id":"2301.10536","title":"Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective","created":"2023-01-25","cited_by_count":0,"area":null},{"id":"2402.07340","title":"Perfect Recovery for Random Geometric Graph Matching with Shallow Graph Neural Networks","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2411.11162","title":"RPN 2: On Interdependence Function Learning Towards Unifying and Advancing CNN, RNN, GNN, and Transformer","created":"2024-11-17","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":"2502.00716","title":"UPL: Uncertainty-aware Pseudo-labeling for Imbalance Transductive Node Classification","created":"2025-02-02","cited_by_count":0,"area":null},{"id":"2507.05533","title":"Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2509.06600","title":"PAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification","created":"2025-09-08","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":"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":"2604.00918","title":"Generalization Bounds for Spectral GNNs via Fourier Domain Analysis","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2605.13597","title":"Rethinking Generalization in Graph Neural Networks: A Structural Complexity Perspective","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.25452","title":"Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks","created":"2026-05-25","cited_by_count":0,"area":null},{"id":"2606.00716","title":"Graph Transfer Learning via Shared Latent Geometry: Theory and Applications","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.06293","title":"PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis","created":"2026-06-04","cited_by_count":0,"area":null}],"next_cursor":null}