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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 amidst non‐IID graph data: A literature review
The growing importance of understanding and addressing algorithmic bias in artificial
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …
intelligence (AI) has led to a surge in research on AI fairness, which often assumes that the …
Trustworthy graph neural networks: Aspects, methods, and trends
Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …
methods for diverse real-world scenarios, ranging from daily applications such as …
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 …
Edits: Modeling and mitigating data bias for graph neural networks
Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed
networks in various web-based applications such as social recommendation and web …
networks in various web-based applications such as social recommendation and web …
Learning fair node representations with graph counterfactual fairness
Fair machine learning aims to mitigate the biases of model predictions against certain
subpopulations regarding sensitive attributes such as race and gender. Among the many …
subpopulations regarding sensitive attributes such as race and gender. Among the many …
Fairdrop: Biased edge dropout for enhancing fairness in graph representation learning
Graph representation learning has become a ubiquitous component in many scenarios,
ranging from social network analysis to energy forecasting in smart grids. In several …
ranging from social network analysis to energy forecasting in smart grids. In several …
Achieving fairness at no utility cost via data reweighing with influence
With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …
property for machine learning models to suppress unintentional discrimination. In this paper …
Demystifying uneven vulnerability of link stealing attacks against graph neural networks
While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …
real-world applications, they have been shown to be vulnerable to a growing number of …
On generalized degree fairness in graph neural networks
Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …
may stem from their input, including node attributes and neighbors surrounding a node …