{"total":53,"rows":[{"id":"2204.08570","title":"A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability","created":"2022-04-18","cited_by_count":118,"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":"2204.09888","title":"Fairness in Graph Mining: A Survey","created":"2022-04-21","cited_by_count":76,"area":null},{"id":"2205.13619","title":"Fairness in Recommendation: Foundations, Methods and Applications","created":"2022-05-26","cited_by_count":27,"area":null},{"id":"2307.04937","title":"Towards Fair Graph Neural Networks via Graph Counterfactual","created":"2023-07-10","cited_by_count":23,"area":null},{"id":"2302.12977","title":"Fair Attribute Completion on Graph with Missing Attributes","created":"2023-02-25","cited_by_count":22,"area":null},{"id":"2304.01391","title":"Counterfactual Learning on Graphs: A Survey","created":"2023-04-03","cited_by_count":22,"area":null},{"id":"2307.03929","title":"Fairness-Aware Graph Neural Networks: A Survey","created":"2023-07-08","cited_by_count":22,"area":null},{"id":"2202.07170","title":"Fairness Amidst Non-IID Graph Data: A Literature Review","created":"2022-02-15","cited_by_count":20,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2206.12104","title":"On Structural Explanation of Bias in Graph Neural Networks","created":"2022-06-24","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":"2405.07011","title":"Fair Graph Representation Learning via Sensitive Attribute Disentanglement","created":"2024-05-11","cited_by_count":13,"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":"2306.04107","title":"BeMap: Balanced Message Passing for Fair Graph Neural Network","created":"2023-06-07","cited_by_count":11,"area":null},{"id":"2407.11624","title":"Rethinking Fair Graph Neural Networks from Re-balancing","created":"2024-07-16","cited_by_count":11,"area":null},{"id":"2212.12040","title":"Graph Learning with Localized Neighborhood Fairness","created":"2022-12-22","cited_by_count":10,"area":null},{"id":"2402.12161","title":"Endowing Pre-trained Graph Models with Provable Fairness","created":"2024-02-19","cited_by_count":10,"area":null},{"id":"2311.05420","title":"Counterfactually Fair Representation","created":"2023-11-09","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":"2403.06017","title":"Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark","created":"2024-03-09","cited_by_count":7,"area":null},{"id":"2305.15622","title":"GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint","created":"2023-05-25","cited_by_count":6,"area":null},{"id":"2402.04383","title":"FairWire: Fair Graph Generation","created":"2024-02-06","cited_by_count":6,"area":null},{"id":"2409.08658","title":"Promoting Fairness in Link Prediction with Graph Enhancement","created":"2024-09-13","cited_by_count":6,"area":null},{"id":"2312.12477","title":"When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook","created":"2023-12-19","cited_by_count":4,"area":null},{"id":"2406.13966","title":"Causal Inference with Latent Variables: Recent Advances and Future Prospectives","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2402.10762","title":"On Explaining Unfairness: An Overview","created":"2024-02-16","cited_by_count":3,"area":null},{"id":"2406.00987","title":"Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2406.03255","title":"On the Maximal Local Disparity of Fairness-Aware Classifiers","created":"2024-06-05","cited_by_count":3,"area":null},{"id":"2407.13251","title":"Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2209.03904","title":"Analyzing the Effect of Sampling in GNNs on Individual Fairness","created":"2022-09-08","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":"2412.09947","title":"Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes","created":"2024-12-13","cited_by_count":2,"area":null},{"id":"2601.20449","title":"Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations","created":"2026-01-28","cited_by_count":2,"area":null},{"id":"2403.06936","title":"Counterfactual Reasoning with Knowledge Graph Embeddings","created":"2024-03-11","cited_by_count":1,"area":null},{"id":"2404.06090","title":"Fair Graph Neural Network with Supervised Contrastive Regularization","created":"2024-04-09","cited_by_count":1,"area":null},{"id":"2404.17511","title":"Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks","created":"2024-04-26","cited_by_count":1,"area":null},{"id":"2409.01367","title":"Debiasing Graph Representation Learning based on Information Bottleneck","created":"2024-09-02","cited_by_count":1,"area":null},{"id":"2411.04371","title":"ComFairGNN: Community Fair Graph Neural Network","created":"2024-11-07","cited_by_count":1,"area":null},{"id":"2508.14683","title":"Improving Fairness in Graph Neural Networks via Counterfactual Debiasing","created":"2025-08-20","cited_by_count":1,"area":null},{"id":"2306.04212","title":"Migrate Demographic Group For Fair GNNs","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2402.10468","title":"Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation","created":"2024-02-16","cited_by_count":0,"area":null},{"id":"2505.01652","title":"Causally Fair Node Classification on Non-IID Graph Data","created":"2025-05-03","cited_by_count":0,"area":null},{"id":"2508.10785","title":"Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection","created":"2025-08-14","cited_by_count":0,"area":null},{"id":"2508.15499","title":"Let's Grow an Unbiased Community: Guiding the Fairness of Graphs via New Links","created":"2025-08-21","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":"2510.19257","title":"FnRGNN: Distribution-aware Fairness in Graph Neural Network","created":"2025-10-22","cited_by_count":0,"area":null},{"id":"2510.23469","title":"Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2512.03074","title":"Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Information","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2604.02342","title":"Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2606.00700","title":"COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2607.05103","title":"Choosing a parallel heterogeneous ensemble method for tabular classification","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.19381","title":"Fairness-Aware Network Embeddings: Methods, Applications, and Challenges","created":"2026-08-19","cited_by_count":0,"area":null}],"next_cursor":null}