A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability
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 …
their great ability in modeling graph-structured data, GNNs are vastly used in various …
Fairness in graph mining: A survey
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 …
However, despite their promising performance on various graph analytical tasks, most of …
Improving fairness in graph neural networks via mitigating sensitive attribute leakage
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 …
on graphs. However, they may inherit historical prejudices from training data, leading to …
In-processing modeling techniques for machine learning fairness: A survey
Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …
clear benefits in terms of performance, the models could show discrimination against …
Fairsna: Algorithmic fairness in social network analysis
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …
domains, including machine learning, natural language processing, and information …
Fair graph distillation
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 …
computational demands, data distillation for graph data promises to alleviate this issue by …
Fmp: Toward fair graph message passing against topology bias
Despite recent advances in achieving fair representations and predictions through
regularization, adversarial debiasing, and contrastive learning in graph neural networks …
regularization, adversarial debiasing, and contrastive learning in graph neural networks …
Toward fair graph neural networks via real counterfactual samples
Graph neural networks (GNNs) have become pivotal in various critical decision-making
scenarios due to their exceptional performance. However, concerns have been raised that …
scenarios due to their exceptional performance. However, concerns have been raised that …
Towards fair graph neural networks via graph counterfactual
Graph neural networks have shown great ability in representation (GNNs) learning on
graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent …
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
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 …
transfer the knowledge of the labeled source domain to the unlabeled target domain without …