mlsanityData to 7 Oct 2026

Fairness-Aware Graph Filter Design

arXiv 2303.11459 ↗First posted 20 Mar 2023cs.LG cs.SI eess.SPcited by 8

Not on the map. Why?

Abstract (arXiv)

Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant attention recently. However, it has been demonstrated that ML over graphs amplifies the already existing bias towards certain under-represented groups in various decision-making problems due to the information aggregation over biased graph structures. Faced with this challenge, in this paper, we design a fair graph filter that can be employed in a versatile manner for graph-based learning tasks. The design of the proposed filter is based on a bias analysis and its optimality in mitigating bias compared to its fairness-agnostic counterpart is established. Experiments on real-world networks for node classification demonstrate the efficacy of the proposed filter design in mitigating bias, while attaining similar utility and better stability compared to baseline algorithms.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 20 Mar 2023.

Builds on

18 of 33 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 409
  3. 2020Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Informationcited by 168
  4. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  5. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  6. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
  7. 2019Stability Properties of Graph Neural Networkscited by 125
  8. 2018Connecting the Dots: Identifying Network Structure via Graph Signal Processingcited by 111
  9. 2021EDITS: Modeling and Mitigating Data Bias for Graph Neural Networkscited by 92
  10. 2020Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networkscited by 73
  11. 2020Graph signal processing for machine learning: A review and new perspectivescited by 73
  12. 2021FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learningcited by 68
6 more
  1. 2022Graph Filters for Signal Processing and Machine Learning on Graphscited by 53
  2. 2016Autoregressive Moving Average Graph Filteringcited by 35
  3. 2016Stationary Graph Processes and Spectral Estimationcited by 35
  4. 2016Kernel-based Reconstruction of Graph Signalscited by 20
  5. 2021The KL-Divergence between a Graph Model and its Fair I-Projection as a Fairness Regularizercited by 17
  6. 2022FairNorm: Fair and Fast Graph Neural Network Trainingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 3; 2026: 3.

8 papers.

  1. 2023Fairness-Aware Graph Neural Networks: A Surveycited by 22
  2. 2023Fairness-aware Optimal Graph Filter Designcited by 2
  3. 2025Deploying AI for Signal Processing education: Selected challenges and intriguing opportunitiescited by 1
  4. 2025FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Trainingcited by 0
  5. 2025Model-Agnostic Fairness Regularization for GNNs with Incomplete Sensitive Informationcited by 0
  6. 2026Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Networkcited by 0
  7. 2026Geometrical fairness in graph neural networkscited by 0
  8. 2026Fairness-Aware Network Embeddings: Methods, Applications, and Challengescited by 0