{"total":69,"rows":[{"id":"2202.02466","title":"Handling Distribution Shifts on Graphs: An Invariance Perspective","created":"2022-02-05","cited_by_count":164,"area":null},{"id":"2202.07987","title":"Out-Of-Distribution Generalization on Graphs: A Survey","created":"2022-02-16","cited_by_count":97,"area":null},{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2209.14107","title":"Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure","created":"2022-09-28","cited_by_count":83,"area":null},{"id":"2204.09888","title":"Fairness in Graph Mining: A Survey","created":"2022-04-21","cited_by_count":76,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2302.02914","title":"Energy-based Out-of-Distribution Detection for Graph Neural Networks","created":"2023-02-06","cited_by_count":68,"area":null},{"id":"2212.09034","title":"Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs","created":"2022-12-18","cited_by_count":42,"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":"2202.04187","title":"FMP: Toward Fair Graph Message Passing against Topology Bias","created":"2022-02-08","cited_by_count":35,"area":null},{"id":"2401.14155","title":"Alleviating Structural Distribution Shift in Graph Anomaly Detection","created":"2024-01-25","cited_by_count":32,"area":null},{"id":"2402.11494","title":"Graph Out-of-Distribution Generalization via Causal Intervention","created":"2024-02-18","cited_by_count":31,"area":null},{"id":"2302.03881","title":"On Generalized Degree Fairness in Graph Neural Networks","created":"2023-02-08","cited_by_count":28,"area":null},{"id":"2201.08549","title":"Fair Node Representation Learning via Adaptive Data Augmentation","created":"2022-01-21","cited_by_count":27,"area":null},{"id":"2307.03929","title":"Fairness-Aware Graph Neural Networks: A Survey","created":"2023-07-08","cited_by_count":22,"area":null},{"id":"2212.04983","title":"Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks","created":"2022-12-09","cited_by_count":21,"area":null},{"id":"2205.05396","title":"A Survey on Fairness for Machine Learning on Graphs","created":"2022-05-11","cited_by_count":20,"area":null},{"id":"2312.04693","title":"GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts","created":"2023-12-07","cited_by_count":19,"area":"moe"},{"id":"2502.06808","title":"On the Benefits of Attribute-Driven Graph Domain Adaptation","created":"2025-02-04","cited_by_count":19,"area":null},{"id":"2211.14383","title":"Interpreting Unfairness in Graph Neural Networks via Training Node Attribution","created":"2022-11-25","cited_by_count":18,"area":null},{"id":"2212.12070","title":"RouteNet-Fermi: Network Modeling with Graph Neural Networks","created":"2022-12-22","cited_by_count":17,"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":"2302.05995","title":"Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview","created":"2023-02-12","cited_by_count":15,"area":null},{"id":"2308.13821","title":"A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions","created":"2023-08-26","cited_by_count":15,"area":null},{"id":"2306.03256","title":"Explaining and Adapting Graph Conditional Shift","created":"2023-06-05","cited_by_count":14,"area":null},{"id":"2306.05557","title":"On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks","created":"2023-06-08","cited_by_count":13,"area":null},{"id":"2201.09391","title":"Partition-Based Active Learning for Graph Neural Networks","created":"2022-01-23","cited_by_count":12,"area":null},{"id":"2209.05957","title":"Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks","created":"2022-09-13","cited_by_count":12,"area":null},{"id":"2303.14591","title":"FairGAT: Fairness-aware Graph Attention Networks","created":"2023-03-26","cited_by_count":12,"area":null},{"id":"2306.11264","title":"GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks","created":"2023-06-20","cited_by_count":12,"area":null},{"id":"2312.12369","title":"Chasing Fairness in Graphs: A GNN Architecture Perspective","created":"2023-12-19","cited_by_count":12,"area":null},{"id":"2505.20089","title":"Homophily Enhanced Graph Domain Adaptation","created":"2025-05-26","cited_by_count":9,"area":null},{"id":"2401.14702","title":"FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently","created":"2024-01-26","cited_by_count":8,"area":null},{"id":"2302.03534","title":"On the Limitation and Experience Replay for GNNs in Continual Learning","created":"2023-02-07","cited_by_count":7,"area":null},{"id":"2309.17337","title":"Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools","created":"2023-09-29","cited_by_count":7,"area":null},{"id":"2310.06417","title":"Supercharging Graph Transformers with Advective Diffusion","created":"2023-10-10","cited_by_count":7,"area":"diffusion"},{"id":"2403.07310","title":"How does promoting the minority fraction affect generalization? A theoretical study of the one-hidden-layer neural network on group imbalance","created":"2024-03-12","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":"2410.04287","title":"Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks","created":"2024-10-05","cited_by_count":6,"area":null},{"id":"2203.15802","title":"Shift-Robust Node Classification via Graph Adversarial Clustering","created":"2022-03-07","cited_by_count":5,"area":null},{"id":"2405.19022","title":"Towards Standardizing AI Bias Exploration","created":"2024-05-29","cited_by_count":5,"area":null},{"id":"2505.11298","title":"Graph Representational Learning: When Does More Expressivity Hurt Generalization?","created":"2025-05-16","cited_by_count":5,"area":null},{"id":"2205.09977","title":"FairNorm: Fair and Fast Graph Neural Network Training","created":"2022-05-20","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":"2511.12913","title":"CoS: Towards Optimal Event Scheduling via Chain-of-Scheduling","created":"2025-11-17","cited_by_count":4,"area":null},{"id":"2305.15822","title":"Towards Label Position Bias in Graph Neural Networks","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2307.08910","title":"Sharpness-Aware Graph Collaborative Filtering","created":"2023-07-18","cited_by_count":3,"area":null},{"id":"2310.05105","title":"How Graph Neural Networks Learn: Lessons from Training Dynamics","created":"2023-10-08","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":"2310.14432","title":"Fairness-aware Optimal Graph Filter Design","created":"2023-10-22","cited_by_count":2,"area":null},{"id":"2310.14527","title":"Marginal Nodes Matter: Towards Structure Fairness in Graphs","created":"2023-10-23","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":"2406.04963","title":"Learning Divergence Fields for Shift-Robust Graph Representations","created":"2024-06-07","cited_by_count":2,"area":null},{"id":"2508.14338","title":"On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks","created":"2025-08-20","cited_by_count":2,"area":null},{"id":"2208.03578","title":"Generalizability Analysis of Graph-based Trajectory Predictor with Vectorized Representation","created":"2022-08-06","cited_by_count":1,"area":null},{"id":"2303.08157","title":"Graph Neural Network Surrogates of Fair Graph Filtering","created":"2023-03-14","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":"2503.18195","title":"Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2602.10489","title":"Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptation","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2204.11164","title":"Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detection","created":"2022-04-24","cited_by_count":0,"area":null},{"id":"2502.15890","title":"Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2503.14571","title":"Efficient Data Selection for Training Genomic Perturbation Models","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2506.18696","title":"SaGIF: Improving Individual Fairness in Graph Neural Networks via Similarity Encoding","created":"2025-06-23","cited_by_count":0,"area":null},{"id":"2506.19281","title":"Robust OOD Graph Learning via Mean Constraints and Noise Reduction","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2510.07536","title":"Estimating Fair Graphs from Graph-Stationary Data","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2510.18370","title":"Training Diverse Graph Experts for Ensembles: A Systematic Empirical Study","created":"2025-10-21","cited_by_count":0,"area":"moe"},{"id":"2608.14823","title":"Disentangling Homophily and Rarity: Explaining Failure in Graph Neural Networks","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.26451","title":"Toward Equitable Low-Carbon Mobility: Fairness-Aware Demand Prediction for Expanding Bike-Sharing Systems","created":"2026-08-26","cited_by_count":0,"area":null}],"next_cursor":null}