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.
By Yuchang Zhu, Zezhong Xie, Huizhe Zhang, Huazhen Zhong, Jintang Li, Liang Chen, Zibin Zheng
arXiv:2504. 21296v2 Announce Type: replace Abstract: Graph learning has evolved into Augmented Graph Learning (AGL) by integrating specialized machine learning (ML) techniques.
By Renqiang Luo, Huafei Huang, Ziqi Xu, Xikun Zhang, Enyan Dai, Bo Yang, Feng Xia
arXiv:2606. 17684v1 Announce Type: cross Abstract: Graph-based learning methods have become increasingly prominent due to their strong performance across diverse applications.
By Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Blas Kolic, Rosa E. Lillo
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).
By Yuhan Yang, Xingbo Fu, Jundong Li
The paper proposes a fairness-aware Mixture-of-Experts (MoE) framework that tackles routing-induced bias by applying subgroup reweighting to correct data imbalance and gate entropy regularization to prevent the gating network from collapsing onto subgroup attributes. This end-to-end approach keeps expert utilization balanced and interpretable, offering a clear view of how subgroups are allocated across experts. Experiments show that the method improves fairness while maintaining competitive predictive performance.
By Sunhee Hwang
FairRARI is a new in‑processing convex optimization framework that computes PageRank vectors while satisfying various group‑fairness constraints based on vertex attributes. By formulating PageRank as a variational problem, FairRARI solves a strongly convex problem that guarantees a target fairness level and retains the same asymptotic time complexity as standard PageRank. Experiments on real datasets show that FairRARI outperforms existing methods in utility while achieving the desired fairness across multiple vertex groups.
By Emmanouil Kariotakis, Aritra Konar