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https://github.com/EdisonLeeeee/Awesome-Fair-Graph-Learning

Paper List for Fair Graph Learning (FairGL).
https://github.com/EdisonLeeeee/Awesome-Fair-Graph-Learning

List: Awesome-Fair-Graph-Learning

fairness graph-learning graph-neural-networks

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Paper List for Fair Graph Learning (FairGL).

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# Awesome Fair Graph Learning

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)[![Contributions Welcome](https://img.shields.io/badge/Contributions-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)



![demo](demo.jpg)



## 2024



+ FairGT: A Fairness-aware Graph Transformer, [[IJCAI]](https://arxiv.org/abs/2404.17169), [[Code]](https://github.com/yushuowiki/fairgt)

+ Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2404.17511)

+ The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation, [[WSDM]](https://arxiv.org/abs/2311.17373), [[Code]](https://github.com/zzoomd/fairgkd)

+ No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation, [[AAAI]](https://arxiv.org/abs/2312.10080)

+ Chasing Fairness in Graphs: A GNN Architecture Perspective, [[AAAI]](https://arxiv.org/abs/2312.12369), [[Code]](https://github.com/zhimengj0326/FMP)

+ Towards Fair Graph Federated Learning via Incentive Mechanisms, [[AAAI]](https://arxiv.org/abs/2312.13306), [[Code]](https://github.com/zjunet/fairgraphfl)

+ Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach, [[ICLR]](https://arxiv.org/abs/2401.10632)

+ MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage, [[arXiv]](https://arxiv.org/abs/2401.12824)

+ Graph Fairness Learning under Distribution Shifts, [[WWW]](https://arxiv.org/abs/2401.16784), [[Code]](https://github.com/liushiliushi/FatraGNN)

+ Disambiguated Node Classification with Graph Neural Networks, [[WWW]](https://arxiv.org/abs/2402.08824), [[Code]](https://github.com/tianxiangzhao/disambiguatedgnn)

+ Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering, [[PAKDD]](https://arxiv.org/abs/2402.10756), [[Code]](https://github.com/siamakghodsi/ifairnmtf)

+ GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2402.12937)

+ Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization, [[AAAI]](https://arxiv.org/abs/2403.12474), [[Code]](https://github.com/BUPT-GAMMA/FairSIN)

+ Towards Fair Graph Anomaly Detection: Problem, New Datasets, and Evaluation, [[arXiv]](https://arxiv.org/abs/2402.15988), [[Code]](https://github.com/nigelnnk/fairgad)

+ Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark, [[arXiv]](https://arxiv.org/abs/2403.06017), [[Code]](https://github.com/xweiq/benchmark-graphfairness)

+ Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2404.03139), [[Code]](https://github.com/ArjunSubramonian/degree-bias-exploration)

+ Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement, [[arXiv]](https://arxiv.org/abs/2406.00987), [[Code]](https://github.com/AhaChang/DEFEND)

+ Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections, [[arXiv]](https://arxiv.org/abs/2406.03052), [[Code]](https://github.com/LuoZhhh/NIFA)

+ Endowing Pre-trained Graph Models with Provable Fairness, [[WWW]](https://arxiv.org/abs/2402.12161), [[Code]](https://github.com/BUPT-GAMMA/GraphPAR)

+ One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes, [[KDD]](https://arxiv.org/abs/2406.13544), [[Code]](https://github.com/ZzoomD/FairINV/)

+ Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach, [[arXiv]](https://arxiv.org/abs/2406.13201)

+ Mind the Graph When Balancing Data for Fairness or Robustness, [[arXiv]](https://arxiv.org/abs/2406.17433)

+ Rethinking Fair Graph Neural Networks from Re-balancing, [[KDD]](https://arxiv.org/abs/2407.11624), [[Code]](https://github.com/zhixunlee/fairgb)

+ Fair Augmentation for Graph Collaborative Filtering, [[arXiv]](https://arxiv.org/abs/2408.12208), [[Code]](https://github.com/jackmedda/FA4GCF)

+ Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2408.12875)

+ Promoting Fairness in Link Prediction with Graph Enhancement, [[arXiv]](https://arxiv.org/abs/2409.08658)



## 2023

+ Interpreting Unfairness in Graph Neural Networks via Training Node Attribution, [[AAAI]](https://arxiv.org/abs/2211.14383), [[Code]](https://github.com/yushundong/BIND)

+ On Generalized Degree Fairness in Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2302.03881)

+ Fair Attribute Completion on Graph with Missing Attributes, [[arXiv]](https://arxiv.org/abs/2302.12977)

+ Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2302.11479)

+ Graph Neural Network Surrogates of Fair Graph Filtering, [[arXiv]](https://arxiv.org/abs/2303.08157)

+ Learning Fair Graph Representations via Automated Data Augmentations, [[ICLR]](https://openreview.net/forum?id=1_OGWcP1s9w)

+ FairGen: Towards Fair Graph Generation, [[arXiv]](https://arxiv.org/abs/2303.17743)

+ Fair Evaluation of Graph Markov Neural Networks, [[arXiv]](http://arxiv.org/abs/2304.01235)

+ GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint, [[arXiv]](https://arxiv.org/abs/2305.15622)

+ Towards Label Position Bias in Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2305.15822)

+ BeMap: Balanced Message Passing for Fair Graph Neural Network, [[arXiv]](https://arxiv.org/abs/2306.04107)

+ Fairness-aware Message Passing for Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2306.11132)

+ Improving Fairness of Graph Neural Networks: A Graph Counterfactual Perspective, [[arXiv]](https://arxiv.org/abs/2307.04937)

+ Fairness-Aware Graph Neural Networks: A Survey, [[arXiv]](https://arxiv.org/abs/2307.03929)

+ Adversarial Attacks on Fairness of Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2310.13822)

+ Fairness-aware Optimal Graph Filter Design, [[arXiv]](https://arxiv.org/abs/2310.14432)

+ Marginal Nodes Matter: Towards Structure Fairness in Graphs, [[arXiv]](https://arxiv.org/abs/2310.14527)

+ Deceptive Fairness Attacks on Graphs via Meta Learning, [[arXiv]](https://arxiv.org/abs/2310.15653)

+ ELEGANT: Certified Defense on the Fairness of Graph Neural Networks, [[arXiv]](https://arxiv.org/abs/2311.02757), [[Code]](https://github.com/yushundong/ELEGANT)

+ A Unified Framework for Fair Spectral Clustering With Effective Graph Learning, [[arXiv]](https://arxiv.org/abs/2311.13766)

+ The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation, [[WSDM]](https://arxiv.org/abs/2311.17373), [[Code]](https://github.com/zzoomd/fairgkd)

+ Understanding Community Bias Amplification in Graph Representation Learning, [[arXiv]](https://arxiv.org/abs/2312.04883)

+ Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction, [[ICML, NeurIPS GLFrontiers]](https://openreview.net/forum?id=9ADkymyCPA)

+ FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently, [[TKDE]](https://arxiv.org/abs/2401.14702)



## 2022

+ Fairness Amidst Non-IID Graph Data: A Literature Review, [[arXiv]](https://arxiv.org/abs/2202.07170)

+ Learning Fair Node Representations with Graph Counterfactual Fairness, [[WSDM]](https://arxiv.org/abs/2201.03662)

+ FMP: Toward Fair Graph Message Passing against Topology Bias, [[arXiv]](https://arxiv.org/abs/2202.04187)

+ Debiased Graph Neural Networks with Agnostic Label Selection Bias, [[TNNLS]](https://arxiv.org/abs/2201.07708), [[Code]](https://github.com/googlebaba/TNNLS2022-DGNN)

+ FairRankVis: A Visual Analytics Framework for Exploring Algorithmic Fairness in Graph Mining Models, [[IEEE Trans. Vis. Comput. Graph.]](https://pubmed.ncbi.nlm.nih.gov/34587074/), [[Code]](https://github.com/tiankaixie/fairrankvis)

+ FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing, [[arXiv]](https://arxiv.org/abs/2201.03681), [[Code]](https://github.com/royull/FairEdit)

+ RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network, [[WWW]](https://arxiv.org/abs/2202.13547), [[Code]](https://github.com/jiank2/RawlsGCN)

+ Fair Graph Representation Learning with Imbalanced and Biased Data, [[WSDM]](https://dl.acm.org/doi/abs/10.1145/3488560.3502218)

+ FairMod: Fair Link Prediction and Recommendation via Graph Modification, [[arXiv]](https://arxiv.org/abs/2201.11596)

+ Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness, [[AAAI]](https://arxiv.org/abs/2202.10816)

+ Fair Node Representation Learning via Adaptive Data Augmentation, [[arXiv]](https://arxiv.org/abs/2201.08549)

+ Subgroup Fairness in Graph-based Spam Detection, [[arXiv]](https://arxiv.org/abs/2204.11164)

+ FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings, [[ACM TIST]](https://arxiv.org/abs/2205.00313)

+ (Survey) A Survey on Fairness for Machine Learning on Graphs, [[arXiv]](https://arxiv.org/abs/2205.05396)

+ FairNorm: Fair and Fast Graph Neural Network Training, [[arXiv]](https://arxiv.org/abs/2205.09977)

+ Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage, [[arXiv]](https://arxiv.org/abs/2206.03426), [[Code]](https://github.com/YuWVandy/FairVGNN)

+ On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods, [[KDD workshop]](https://arxiv.org/abs/2207.04376) 

+ GUIDE: Group Equality Informed Individual Fairness in Graph Neural Network, [[KDD]](http://www.ece.virginia.edu/~jl6qk/paper/KDD22_Group_Informed_Individual_Fairness.pdf), [[Code]](https://github.com/weihaosong/GUIDE)

+ Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks, [[ICDM]](https://arxiv.org/abs/2209.05957), [[Code]](https://github.com/mengcao327/attack-gnn-fairness)

+ Uncovering the Structural Fairness in Graph Contrastive Learning, [[NeurIPS]](https://arxiv.org/abs/2210.03011), [[Code]](https://github.com/BUPT-GAMMA/Uncovering-the-Structural-Fairness-in-Graph-Contrastive-Learning)

+ Item-based Variational Auto-encoder for Fair Music Recommendation, [[CIKM]](https://arxiv.org/abs/2211.01333)

+ Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers, [[IEEE Big Data]](https://arxiv.org/abs/2211.00783)

+ Graph Learning with Localized Neighborhood Fairness, [[arXiv]](https://arxiv.org/abs/2212.12040)

+ Graph Self-supervised Learning with Accurate Discrepancy Learning, [[NeurIPS]](https://arxiv.org/abs/2202.02989), [[Code]](https://github.com/DongkiKim95/D-SLA)

+ On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs, [[NeurIPS]](https://proceedings.neurips.cc/paper_files/paper/2022/hash/d4c2f25bf0c33065b7d4fb9be2a9add1-Abstract-Conference.html)



## 2021

+ FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, [[arXiv]](https://arxiv.org/abs/2104.14210), [[Code]](https://github.com/ispamm/FairDrop)

+ On Dyadic Fairness: Exploring and Mitigating Bias in Graph Connections, [[ICLR]](https://openreview.net/forum?id=xgGS6PmzNq6), [[Code]](https://github.com/brandeis-machine-learning/FairAdj)

+ Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information, [[WSDM]](https://arxiv.org/abs/2009.01454), [[Code]](https://github.com/EnyanDai/FairGNN)

+ Subgroup Generalization and Fairness of Graph Neural Networks, [[NeurIPS]](https://arxiv.org/abs/2106.15535), [[Code]](https://github.com/TheaperDeng/GNN-Generalization-Fairness)

+ Towards a Unified Framework for Fair and Stable Graph Representation Learning, [[UAI]](https://arxiv.org/abs/2102.13186)

+ Individual Fairness for Graph Neural Networks: A Ranking based Approach, [[KDD]](https://dl.acm.org/doi/abs/10.1145/3447548.3467266), [[Code]](https://github.com/yushundong/REDRESS)

+ Fair Representation Learning for Heterogeneous Information Networks, [[ICWSM]](https://arxiv.org/abs/2104.08769), [[Code]](https://github.com/HKUST-KnowComp/Fair_HIN)

+ All of the Fairness for Edge Prediction with Optimal Transport, [[AISTATS]](https://arxiv.org/abs/2010.16326)

+ CrossWalk: Fairness-enhanced Node Representation Learning, [[arXiv]](https://arxiv.org/abs/2105.02725)

+ The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizer, [[ECML-PKDD]](https://arxiv.org/abs/2103.01846)

+ Certification and Trade-off of Multiple Fairness Criteria in Graph-based Spam Detection, [[CIKM]](https://dl.acm.org/doi/abs/10.1145/3459637.3482325)

+ Post-processing for Individual Fairness, [[NeurIPS]](https://arxiv.org/abs/2110.13796), [[Code]](https://github.com/Felix-Petersen/fairness-post-processing)

+ FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings, [[ACM Trans. Intell. Syst. Technol]](https://dl.acm.org/doi/10.1145/3495163)

+ Prior Signal Editing for Graph Filter Posterior Fairness Constraints, [[arXiv]](https://arxiv.org/abs/2108.12397)

+ Fairness-Aware Node Representation Learning, [[arXiv]](https://arxiv.org/abs/2106.05391)

+ Fairness-Aware Recommendation in Multi-Sided Platforms, [[WSDM]](https://dl.acm.org/doi/10.1145/3437963.3441672)

+ Fairness Violations and Mitigation under Covariate Shift, [[ACM FAccT]](https://arxiv.org/abs/1911.00677)

+ Fair Graph Auto-Encoder for Unbiased Graph Representations with Wasserstein Distance, [[ICDM]](https://ieeexplore.ieee.org/document/9679109)

+ A Multi-view Confidence-calibrated Framework for Fair and Stable Graph Representation Learning, [[ICDM]](https://ieeexplore.ieee.org/document/9679093)

+ Learning Fair Representations for Recommendation: A Graph-based Perspective, [[WWW]](https://arxiv.org/abs/2102.09140), [[Code]](https://github.com/newlei/FairGo)



## 2020

+ Debiasing knowledge graph embeddings, [[EMNLP]](https://aclanthology.org/2020.emnlp-main.595.pdf)

+ Fairness-Aware Explainable Recommendation over Knowledge Graphs, [[SIGIR]](https://arxiv.org/abs/2006.02046), [[Code]](https://github.com/zuohuif/FairKG4Rec)

+ InFoRM: Individual Fairness on Graph Mining, [[KDD]](https://dl.acm.org/doi/abs/10.1145/3394486.3403080), [[Code]](https://github.com/jiank2/inform)

+ A Unifying Framework for Fairness-Aware Influence Maximization, [[WWW]](https://dl.acm.org/doi/10.1145/3366424.3383555)

+ Applying Fairness Constraints on Graph Node Ranks Under Personalization Bias, [[COMPLEX NETWORKS]](https://link.springer.com/chapter/10.1007/978-3-030-65351-4_49)



## 2019

+ Fairwalk: Towards Fair Graph Embedding, [[IJCAI]](https://www.ijcai.org/proceedings/2019/456), [[Code]](https://github.com/EnderGed/Fairwalk)

+ Compositional Fairness Constraints for Graph Embeddings, [[ICML]](https://arxiv.org/abs/1905.10674), [[Code]](https://github.com/joeybose/Flexible-Fairness-Constraints)

+ Exploring Algorithmic Fairness in Robust Graph Covering Problems, [[NeurIPS]](https://arxiv.org/abs/2006.06865), [[Code]](https://github.com/Aida-Rahmattalabi/FairGraphCovering)