Geometrical fairness in graph neural networks

arXiv:2606.17684 · stat.ML, cs.CY, cs.LG · Submitted 2026-06-16 · Read on arXiv

Arturo Pérez-Peralta, Sandra Benítez-Peña, Blas Kolic, Rosa E. Lillo

stat.ML, cs.CY, cs.LG

Submitted: 2026-06-16

Comments: 32 pages, 21 tables, 6 figures

Code: https://github.com/propublica/compas-analysis

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 27× in this paper · explained in 1 episode
• AUC — 18× in this paper · explained in 2 episodes
• XGBoost — 15× in this paper · explained in 3 episodes
• Neural Sheaf Diffusion — 12× in this paper
• GNNs — 8× in this paper · explained in 2 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Time-Varying System — 7× in this paper
• Spectral Gap — 5× in this paper · explained in 6 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• F1 Score — 4× in this paper · explained in 4 episodes
• Representation Learning — 4× in this paper · explained in 3 episodes
• Accountability — 3× in this paper · explained in 2 episodes
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Graph Convolutional Networks — 3× in this paper
• Lyapunov function — 3× in this paper · explained in 2 episodes
• Over-smoothing — 3× in this paper · explained in 4 episodes
• Ablation Study — 2× in this paper · explained in 3 episodes
• Chebyshev Polynomials — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Diffusion Processes — 2× in this paper · explained in 2 episodes
• Expressivity — 2× in this paper · explained in 2 episodes
• Graph Neural Networks (GNNs) — 2× in this paper · explained in 13 episodes
• Localization — 2× in this paper · explained in 2 episodes
• Over-squashing — 2× in this paper · explained in 3 episodes
• Stochastic Block Model — 2× in this paper
• Weight Decay — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Fair Spectral Filter — 1× in this paper
• Fair Spectral Projection — 1× in this paper
• Fair Vector Projection — 1× in this paper
• Hyperparameters — 1× in this paper · explained in 2 episodes
• Knowledge Graph — 1× in this paper · explained in 8 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Pareto Frontier — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Residual Connections — 1× in this paper · explained in 3 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 6 episodes

Sources

• Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves
• FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph Editing
• On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
• Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs

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