{"total":92,"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":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2206.03426","title":"Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage","created":"2022-06-07","cited_by_count":61,"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":"2302.03881","title":"On Generalized Degree Fairness in Graph Neural Networks","created":"2023-02-08","cited_by_count":28,"area":null},{"id":"2302.02738","title":"INCREASE: Inductive Graph Representation Learning for Spatio-Temporal Kriging","created":"2023-02-06","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":"2212.03840","title":"Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations","created":"2022-12-07","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":"2211.14383","title":"Interpreting Unfairness in Graph Neural Networks via Training Node Attribution","created":"2022-11-25","cited_by_count":18,"area":null},{"id":"2504.10499","title":"Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey","created":"2025-04-08","cited_by_count":18,"area":"rag"},{"id":"2306.17408","title":"LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection","created":"2023-06-30","cited_by_count":17,"area":null},{"id":"2301.12951","title":"Unraveling Privacy Risks of Individual Fairness in Graph Neural Networks","created":"2023-01-30","cited_by_count":16,"area":null},{"id":"2311.17373","title":"The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation","created":"2023-11-29","cited_by_count":16,"area":"distill"},{"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":"2303.14591","title":"FairGAT: Fairness-aware Graph Attention Networks","created":"2023-03-26","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":"2301.01150","title":"RELIANT: Fair Knowledge Distillation for Graph Neural Networks","created":"2023-01-03","cited_by_count":10,"area":"distill"},{"id":"2401.16784","title":"Graph Fairness Learning under Distribution Shifts","created":"2024-01-30","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":"2406.09513","title":"Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior","created":"2024-06-13","cited_by_count":10,"area":null},{"id":"2406.13544","title":"One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes","created":"2024-06-19","cited_by_count":9,"area":null},{"id":"2303.11459","title":"Fairness-Aware Graph Filter Design","created":"2023-03-20","cited_by_count":8,"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":"2203.16413","title":"Learning Fair Models without Sensitive Attributes: A Generative Approach","created":"2022-03-30","cited_by_count":6,"area":null},{"id":"2207.04376","title":"On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods","created":"2022-07-10","cited_by_count":6,"area":null},{"id":"2301.05860","title":"State of the Art and Potentialities of Graph-level Learning","created":"2023-01-14","cited_by_count":6,"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":"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":"2211.09925","title":"FairMILE: Towards an Efficient Framework for Fair Graph Representation Learning","created":"2022-11-17","cited_by_count":4,"area":null},{"id":"2212.08966","title":"Graph Learning and Its Advancements on Large Language Models: A Holistic Survey","created":"2022-12-17","cited_by_count":4,"area":null},{"id":"2310.13822","title":"Adversarial Attacks on Fairness of Graph Neural Networks","created":"2023-10-20","cited_by_count":4,"area":null},{"id":"2412.03706","title":"Fairness without Demographics through Learning Graph of Gradients","created":"2024-12-04","cited_by_count":4,"area":null},{"id":"2601.09394","title":"FairGE: Fairness-Aware Graph Encoding in Incomplete Social Networks","created":"2026-01-14","cited_by_count":4,"area":null},{"id":"2211.00783","title":"Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers","created":"2022-11-01","cited_by_count":3,"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":"2308.09596","title":"Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification","created":"2023-08-18","cited_by_count":3,"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":"2505.09702","title":"Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing","created":"2025-05-14","cited_by_count":3,"area":"safety"},{"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":"2302.11479","title":"Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2310.12350","title":"Equipping Federated Graph Neural Networks with Structure-aware Group Fairness","created":"2023-10-18","cited_by_count":2,"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":"2401.12824","title":"MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage","created":"2024-01-23","cited_by_count":2,"area":null},{"id":"2406.03052","title":"Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections","created":"2024-06-05","cited_by_count":2,"area":null},{"id":"2407.12112","title":"A Benchmark for Fairness-Aware Graph Learning","created":"2024-07-16","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":"2501.11214","title":"Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study","created":"2025-01-20","cited_by_count":2,"area":null},{"id":"2510.18473","title":"Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2602.11802","title":"Structural Bias Beyond Homophily: A Study of Fairness in Link Prediction","created":"2026-02-12","cited_by_count":2,"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":"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":"2412.00382","title":"Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation","created":"2024-11-30","cited_by_count":1,"area":"distill"},{"id":"2510.25096","title":"Learning Fair Graph Representations with Multi-view Information Bottleneck","created":"2025-10-29","cited_by_count":1,"area":null},{"id":"2602.08589","title":"FairRARI: A Plug and Play Framework for Fairness-Aware PageRank","created":"2026-02-09","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":"2301.07639","title":"A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems","created":"2023-01-18","cited_by_count":0,"area":null},{"id":"2306.04212","title":"Migrate Demographic Group For Fair GNNs","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2311.01591","title":"Fair Graph Machine Learning under Adversarial Missingness Processes","created":"2023-11-02","cited_by_count":0,"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":"2410.22120","title":"Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act","created":"2024-10-29","cited_by_count":0,"area":null},{"id":"2501.00595","title":"Unbiased GNN Learning via Fairness-Aware Subgraph Diffusion","created":"2024-12-31","cited_by_count":0,"area":"diffusion"},{"id":"2504.09210","title":"FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training","created":"2025-04-12","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":"2505.14945","title":"Unlearning Algorithmic Biases over Graphs","created":"2025-05-20","cited_by_count":0,"area":"safety"},{"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":"2506.21559","title":"GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations","created":"2025-06-11","cited_by_count":0,"area":null},{"id":"2507.19067","title":"PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems","created":"2025-07-25","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":"2510.27136","title":"FairAD: Computationally Efficient Fair Graph Clustering via Algebraic Distance","created":"2025-10-31","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":"2512.07433","title":"Mitigating Bias in Graph Hyperdimensional Computing","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2601.12751","title":"A Boolean Function-Theoretic Framework for Expressivity in GNNs with Applications to Fair Graph Mining","created":"2026-01-19","cited_by_count":0,"area":null},{"id":"2603.03867","title":"k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoods","created":"2026-03-04","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":"2607.14705","title":"Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics","created":"2026-07-16","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}