Hugging Face Trending Papers
Subgraph Filtering for Fair Graph Neural Networks
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arXiv Machine Learning
Aug 28
Subgraph Filtering for Fair Graph Neural Networks
Subgraph Filtering for Fair Graph Neural Networks (SF‑GNN) is a lightweight, architecture‑agnostic framework that reduces structural bias in GNNs by identifying and filtering bias‑prone edges during message passing. It combines sensitive homophily with structural propagation amplifiers such as hub participation and triadic closure to detect problematic edges, then applies stochastic edge filtering to downweight or remove them while preserving the rest of the graph. Experiments on five benchmark datasets demonstrate that SF‑GNN consistently improves fairness while maintaining competitive predictive performance, achieving a better fairness–accuracy trade‑off than recent fairness‑aware GNN baselines.
arXiv Machine Learning
Aug 4
Fairness in Augmented Graph Learning: A Survey
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
arXiv Machine Learning
Jun 17
Geometrical fairness in graph neural networks
arXiv:2606. 17684v1 Announce Type: cross Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications.
arXiv Machine Learning
Jun 3
Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
arXiv:2510. 23469v2 Announce Type: replace Abstract: Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs).
arXiv Machine Learning
Jul 17
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
arXiv:2607. 14705v1 Announce Type: new Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race.
arXiv AI
Aug 21
Fairness-Aware Network Embeddings: Methods, Applications, and Challenges
arXiv:2608. 19381v1 Announce Type: cross Abstract: Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization.