A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability

E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang… - Machine Intelligence …, 2024 - Springer
Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Fairness in graph mining: A survey

Y Dong, J Ma, S Wang, C Chen… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Improving fairness in graph neural networks via mitigating sensitive attribute leakage

Y Wang, Y Zhao, Y Dong, H Chen, J Li… - Proceedings of the 28th …, 2022 - dl.acm.org
Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …

In-processing modeling techniques for machine learning fairness: A survey

M Wan, D Zha, N Liu, N Zou - ACM Transactions on Knowledge …, 2023 - dl.acm.org
Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Fairsna: Algorithmic fairness in social network analysis

A Saxena, G Fletcher, M Pechenizkiy - ACM Computing Surveys, 2024 - dl.acm.org
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

Fair graph distillation

Q Feng, ZS Jiang, R Li, Y Wang… - Advances in Neural …, 2023 - proceedings.neurips.cc
As graph neural networks (GNNs) struggle with large-scale graphs due to high
computational demands, data distillation for graph data promises to alleviate this issue by …

Fmp: Toward fair graph message passing against topology bias

Z Jiang, X Han, C Fan, Z Liu, N Zou… - arXiv preprint arXiv …, 2022 - arxiv.org
Despite recent advances in achieving fair representations and predictions through
regularization, adversarial debiasing, and contrastive learning in graph neural networks …

Toward fair graph neural networks via real counterfactual samples

Z Wang, M Qiu, M Chen, MB Salem, X Yao… - … and Information Systems, 2024 - Springer
Graph neural networks (GNNs) have become pivotal in various critical decision-making
scenarios due to their exceptional performance. However, concerns have been raised that …

Towards fair graph neural networks via graph counterfactual

Z Guo, J Li, T Xiao, Y Ma, S Wang - Proceedings of the 32nd ACM …, 2023 - dl.acm.org
Graph neural networks have shown great ability in representation (GNNs) learning on
graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent …

Complementary attention-driven contrastive learning with hard-sample exploring for unsupervised domain adaptive person re-id

Y Liu, H Ge, L Sun, Y Hou - … on Circuits and Systems for Video …, 2022 - ieeexplore.ieee.org
Unsupervised domain adaptive (UDA) methods for person re-identification (Re-ID) aim to
transfer the knowledge of the labeled source domain to the unlabeled target domain without …