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Paper List for Fair Graph Learning (FairGL).
License: MIT License
awesome-fair-graph-learning's Introduction
Awesome Fair Graph Learning
- The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation, [WSDM], [Code]
- No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation, [AAAI]
- Chasing Fairness in Graphs: A GNN Architecture Perspective, [AAAI], [Code]
- Towards Fair Graph Federated Learning via Incentive Mechanisms, [AAAI], [Code]
- Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach, [ICLR]
- MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage, [arXiv]
- Graph Fairness Learning under Distribution Shifts, [WWW], [Code]
- Disambiguated Node Classification with Graph Neural Networks, [WWW], [Code]
- Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering, [PAKDD]
- GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks, [arXiv]
- Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization, [AAAI], [Code]
- Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation, [arXiv], [Code]
- Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark, [arXiv], [Code]
- Interpreting Unfairness in Graph Neural Networks via Training Node Attribution, [AAAI], [Code]
- On Generalized Degree Fairness in Graph Neural Networks, [arXiv]
- Fair Attribute Completion on Graph with Missing Attributes, [arXiv]
- Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks, [arXiv]
- Graph Neural Network Surrogates of Fair Graph Filtering, [arXiv]
- Learning Fair Graph Representations via Automated Data Augmentations, [ICLR]
- FairGen: Towards Fair Graph Generation, [arXiv]
- Fair Evaluation of Graph Markov Neural Networks, [arXiv]
- GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint, [arXiv]
- Towards Label Position Bias in Graph Neural Networks, [arXiv]
- BeMap: Balanced Message Passing for Fair Graph Neural Network, [arXiv]
- Fairness-aware Message Passing for Graph Neural Networks, [arXiv]
- Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective, [arXiv]
- Fairness-Aware Graph Neural Networks: A Survey, [arXiv]
- Adversarial Attacks on Fairness of Graph Neural Networks, [arXiv]
- Fairness-aware Optimal Graph Filter Design, [arXiv]
- Marginal Nodes Matter: Towards Structure Fairness in Graphs, [arXiv]
- Deceptive Fairness Attacks on Graphs via Meta Learning, [arXiv]
- ELEGANT: Certified Defense on the Fairness of Graph Neural Networks, [arXiv], [Code]
- A Unified Framework for Fair Spectral Clustering With Effective Graph Learning, [arXiv]
- The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation, [WSDM], [Code]
- Understanding Community Bias Amplification in Graph Representation Learning, [arXiv]
- Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction, [GLFrontiers, NeurIPS]
- FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently, [TKDE]
- Fairness Amidst Non-IID Graph Data: A Literature Review, [arXiv]
- Learning Fair Node Representations with Graph Counterfactual Fairness, [WSDM]
- FMP: Toward Fair Graph Message Passing against Topology Bias, [arXiv]
- Debiased Graph Neural Networks with Agnostic Label Selection Bias, [TNNLS], [Code]
- FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models, [IEEE Trans. Vis. Comput. Graph.], [Code]
- FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing, [arXiv], [Code]
- RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network, [WWW], [Code]
- Fair Graph Representation Learning with Imbalanced and Biased Data, [WSDM]
- FairMod: Fair Link Prediction and Recommendation via Graph Modification, [arXiv]
- Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness, [AAAI]
- Fair Node Representation Learning via Adaptive Data Augmentation, [arXiv]
- Subgroup Fairness in Graph-based Spam Detection, [arXiv]
- FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings, [ACM TIST]
- (Survey) A Survey on Fairness for Machine Learning on Graphs, [arXiv]
- FairNorm: Fair and Fast Graph Neural Network Training, [arXiv]
- Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage, [arXiv], [Code]
- On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods, [KDD workshop]
- GUIDE: Group Equality Informed Individual Fairness in Graph Neural Network, [KDD], [Code]
- Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks, [ICDM], [Code]
- Uncovering the Structural Fairness in Graph Contrastive Learning, [NeurIPS], [Code]
- Item-based Variational Auto-encoder for Fair Music Recommendation, [CIKM]
- Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers, [IEEE Big Data]
- Graph Learning with Localized Neighborhood Fairness, [arXiv]
- Graph Self-supervised Learning with Accurate Discrepancy Learning, [NeurIPS], [Code]
- On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs, [NeurIPS]
- FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, [arXiv], [Code]
- On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections, [ICLR], [Code]
- Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information, [WSDM], [Code]
- Subgroup Generalization and Fairness of Graph Neural Networks, [NeurIPS], [Code]
- Towards a Unified Framework for Fair and Stable Graph Representation Learning, [UAI]
- Individual Fairness for Graph Neural Networks: A Ranking based Approach, [KDD], [Code]
- Fair Representation Learning for Heterogeneous Information Networks, [ICWSM], [Code]
- All of the Fairness for Edge Prediction with Optimal Transport, [AISTATS]
- CrossWalk: Fairness-enhanced Node Representation Learning, [arXiv]
- The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer, [ECML-PKDD]
- Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection, [CIKM]
- Post-processing for Individual Fairness, [NeurIPS], [Code]
- FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings, [ACM Trans. Intell. Syst. Technol]
- Prior Signal Editing for Graph Filter Posterior Fairness Constraints, [arXiv]
- Fairness-Aware Node Representation Learning, [arXiv]
- Fairness-Aware Recommendation in Multi-Sided Platforms, [WSDM]
- Fairness Violations and Mitigation under Covariate Shift, [ACM FAccT]
- Fair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance, [ICDM]
- A Multi-view Confidence-calibrated Framework for Fair and Stable Graph Representation Learning, [ICDM]
- Learning Fair Representations for Recommendation: A Graph-based Perspective, [WWW], [Code]
- Debiasing knowledge graph embeddings, [EMNLP]
- Fairness-Aware Explainable Recommendation over Knowledge Graphs, [SIGIR], [Code]
- InFoRM: Individual Fairness on Graph Mining, [KDD], [Code]
- A Unifying Framework for Fairness-Aware Influence Maximization, [WWW]
- Applying Fairness Constraints on Graph Node Ranks Under Personalization Bias, [COMPLEX NETWORKS]
- Fairwalk: Towards Fair Graph Embedding, [IJCAI], [Code]
- Compositional Fairness Constraints for Graph Embeddings, [ICML], [Code]
- Exploring Algorithmic Fairness in Robust Graph Covering Problems, [NeurIPS], [Code]
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