Marginal Nodes Matter: Towards Structure Fairness in Graphs
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Abstract (arXiv)
In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works on graphs mainly focus on protecting sensitive attributes (e.g., age and gender), the fairness incurred by the graph structure should also be given attention. On the other hand, the information aggregation mechanism of graph neural networks amplifies such structure unfairness, as marginal nodes are often far away from other nodes. In this paper, we focus on novel fairness incurred by the graph structure on graph neural networks, named structure fairness. Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks. Motivated by the observation, we propose Structural Fair Graph Neural Network (SFairGNN), which combines neighborhood expansion based structure debiasing with hop-aware attentive information aggregation to achieve structure fairness. Our experiments show SFairGNN can significantly improve structure fairness while maintaining overall performance in the downstream tasks.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 42 of its 74 references that are in the map. First posted on arXiv 23 Oct 2023.
Builds on
42 of 74 references.
References · 42
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
- 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
- 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
- 2019Simplifying Graph Convolutional Networkscited by 1,477
- 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,102
- 2018Pitfalls of Graph Neural Network Evaluationcited by 1,007
- 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 976
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- 2019Graph Neural Networks for Social Recommendationcited by 714
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2018Deep Learning on Graphs: A Surveycited by 393
- 2020Fairness in Machine Learning: A Surveycited by 370
- 2017Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Rankingcited by 335
- 2019Position-aware Graph Neural Networkscited by 203
- 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
- 2020Algorithmic Fairnesscited by 143
- 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
- 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
- 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
- 2023Everything is Connected: Graph Neural Networkscited by 85
- 2022Fairness in Graph Mining: A Surveycited by 76
- 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
- 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
- 2022Learning Fair Node Representations with Graph Counterfactual Fairnesscited by 58
- 2019Deep Representation Learning for Social Network Analysiscited by 35
- 2022FMP: Toward Fair Graph Message Passing against Topology Biascited by 35
- 2022Uncovering the Structural Fairness in Graph Contrastive Learningcited by 29
- 2023On Generalized Degree Fairness in Graph Neural Networkscited by 28
- 2022Fair Node Representation Learning via Adaptive Data Augmentationcited by 27
- 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
- 2023GiGaMAE: Generalizable Graph Masked Autoencoder via Collaborative Latent Space Reconstructioncited by 21
- 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
- 2022A Survey on Fairness for Machine Learning on Graphscited by 20
- 2023Toward Degree Bias in Embedding-Based Knowledge Graph Completioncited by 17
- 2023Graph Mixup with Soft Alignmentscited by 15
- 2022Graph Contrastive Learning with Personalized Augmentationcited by 14
- 2021Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Predictioncited by 12
- 2022Geometric Graph Representation Learning via Maximizing Rate Reductioncited by 9
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