Repository files navigation Awesome Fair Graph Learning
FairGT: A Fairness-aware Graph Transformer, [IJCAI] , [Code]
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks, [arXiv]
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] , [Code]
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]
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks, [arXiv] , [Code]
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement, [arXiv] , [Code]
Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections, [arXiv] , [Code]
Endowing Pre-trained Graph Models with Provable Fairness, [WWW] , [Code]
One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes, [KDD] , [Code]
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach, [arXiv]
Mind the Graph When Balancing Data for Fairness or Robustness, [arXiv]
Rethinking Fair Graph Neural Networks from Re-balancing, [KDD] , [Code]
Fair Augmentation for Graph Collaborative Filtering, [arXiv] , [Code]
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks, [arXiv]
Promoting Fairness in Link Prediction with Graph Enhancement, [arXiv]
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, [ICML, NeurIPS GLFrontiers]
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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Paper List for Fair Graph Learning (FairGL).
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