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 …

This article provides a comprehensive overview of the fairness issues in artificial intelligence
(AI) systems, delving into its background, definition, and development process. The article …

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 …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

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

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Abstract Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving
graph analytical problems in various real-world applications. Nevertheless, GNNs could …

We consider fair graph representation learning via data augmentations. While this direction
has been explored previously, existing methods invariably rely on certain assumptions on …

Graph neural networks (GNNs) have become pivotal in various critical decision-making
scenarios due to their exceptional performance. However, concerns have been raised that …

Data-driven algorithms are only as good as the data they work with, while datasets,
especially social data, often fail to represent minorities adequately. Representation Bias in …