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 …

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

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

Graph machine learning algorithms have become popular tools in helping us gain a deeper
understanding of the ubiquitous graph data. Despite their effectiveness, most graph machine …

Node representation learning plays a critical role in learning over graphs. Specifically, the
success of contrastive learning methods in unsupervised node representation learning has …

Fairness in Graph Convolutional Neural Networks (GCNs) becomes a more and more
important concern as GCNs are adopted in many crucial applications. Societal biases …

Graph Neural Networks (GNNs) have shown great power in various domains. However, their
predictions may inherit societal biases on sensitive attributes, limiting their adoption in real …

Despite the remarkable success of graph neural networks (GNNs) in modeling graph-
structured data, like other machine learning models, GNNs are also susceptible to making …