mlsanityData to 7 Oct 2026

CrossWalk: Fairness-enhanced Node Representation Learning

arXiv 2105.02725 ↗First posted 6 May 2021cs.LG cs.SI stat.MLcited by 34

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Abstract (arXiv)

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups' peripheries or (2) connect different groups in the network. CrossWalk pulls nodes that are near groups' peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms, including influence maximization, link prediction and node classification in synthetic and real networks, with only a very small decrease in performance.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

12 of 31 references.

References · 12

  1. 2017Inductive Representation Learning on Large Graphscited by 6,284
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  5. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  6. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  7. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  8. 2019Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Makingcited by 75
  9. 2019Operationalizing Individual Fairness with Pairwise Fair Representationscited by 64
  10. 2020DeBayes: a Bayesian Method for Debiasing Network Embeddingscited by 55
  11. 2020Adversarial Graph Embeddings for Fair Influence Maximization over Social Networkscited by 15
  12. 2016On the Efficiency of the Information Networks in Social Mediacited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 8; 2023: 7; 2024: 5; 2025: 6; 2026: 7.

34 papers.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2022Fairness in Graph Mining: A Surveycited by 76
  3. 2021Subgroup Generalization and Fairness of Graph Neural Networkscited by 69
  4. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  5. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  6. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  7. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  8. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  9. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  10. 2023FairGAT: Fairness-aware Graph Attention Networkscited by 12
  11. 2024FUGNN: Harmonizing Fairness and Utility in Graph Neural Networkscited by 11
  12. 2024Rethinking Fair Graph Neural Networks from Re-balancingcited by 11
22 more
  1. 2022Graph Learning with Localized Neighborhood Fairnesscited by 10
  2. 2024One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributescited by 9
  3. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  4. 2022FairMILE: Towards an Efficient Framework for Fair Graph Representation Learningcited by 4
  5. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
  6. 2023Disparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classificationcited by 3
  7. 2024On Explaining Unfairness: An Overviewcited by 3
  8. 2025Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphscited by 2
  9. 2026Structural Bias Beyond Homophily: A Study of Fairness in Link Predictioncited by 2
  10. 2024Fair Graph Neural Network with Supervised Contrastive Regularizationcited by 1
  11. 2025DQ4FairIM: Fairness-aware Influence Maximization using Deep Reinforcement Learningcited by 1
  12. 2025Fairness-aware PageRank via Edge Reweightingcited by 1
  13. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  14. 2025Information Loss and Disparate Effects in Network Embeddingscited by 0
  15. 2025Estimating Fair Graphs from Graph-Stationary Datacited by 0
  16. 2025Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Informationcited by 0
  17. 2026A Boolean Function-Theoretic Framework for Expressivity in GNNs with Applications to Fair Graph Miningcited by 0
  18. 2026FairRARI: A Plug and Play Framework for Fairness-Aware PageRankcited by 0
  19. 2026k-hop Fairness: Addressing Disparities in Graph Link Prediction Beyond First-Order Neighborhoodscited by 0
  20. 2026How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphscited by 0
  21. 2026COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphscited by 0
  22. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0