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 …

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias
often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing …

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 …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Graph Neural Networks (GNNs) have shown superior performance in analyzing attributed
networks in various web-based applications such as social recommendation and web …

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 …

Graph representation learning has become a ubiquitous component in many scenarios,
ranging from social network analysis to energy forecasting in smart grids. In several …

With the fast development of algorithmic governance, fairness has become a compulsory
property for machine learning models to suppress unintentional discrimination. In this paper …

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 …

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 …