{"total":15,"rows":[{"id":"2202.07987","title":"Out-Of-Distribution Generalization on Graphs: A Survey","created":"2022-02-16","cited_by_count":97,"area":null},{"id":"2403.04468","title":"A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges","created":"2024-03-07","cited_by_count":54,"area":null},{"id":"2410.19265","title":"A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation","created":"2024-10-25","cited_by_count":17,"area":null},{"id":"2310.14161","title":"Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation","created":"2023-10-22","cited_by_count":12,"area":null},{"id":"2405.11034","title":"Safety in Graph Machine Learning: Threats and Safeguards","created":"2024-05-17","cited_by_count":11,"area":null},{"id":"2406.13137","title":"Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models","created":"2024-06-19","cited_by_count":7,"area":null},{"id":"2307.11341","title":"OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning","created":"2023-07-21","cited_by_count":5,"area":null},{"id":"2404.14642","title":"Uncertainty Quantification on Graph Learning: A Survey","created":"2024-04-23","cited_by_count":4,"area":null},{"id":"2503.19382","title":"Causal invariant geographic network representations with feature and structural distribution shifts","created":"2025-03-25","cited_by_count":3,"area":null},{"id":"2508.19641","title":"Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses","created":"2025-08-27","cited_by_count":3,"area":null},{"id":"2409.09645","title":"COSCO: A Sharpness-Aware Training Framework for Few-shot Multivariate Time Series Classification","created":"2024-09-15","cited_by_count":2,"area":null},{"id":"2405.20012","title":"FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs","created":"2024-05-30","cited_by_count":1,"area":null},{"id":"2509.00387","title":"Unifying Adversarial Perturbation for Graph Neural Networks","created":"2025-08-30","cited_by_count":1,"area":null},{"id":"2408.06665","title":"RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling","created":"2024-08-13","cited_by_count":0,"area":null},{"id":"2602.08855","title":"Rethinking Graph Generalization through the Lens of Sharpness-Aware Minimization","created":"2026-02-09","cited_by_count":0,"area":null}],"next_cursor":null}