Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-
based learning problem, such as credit risk assessment in financial networks and fake news …

Graph neural networks (GNNs) have achieved remarkable performance on graph-structured
data. However, GNNs may inherit prejudice from the training data and make discriminatory …

There has been significant progress in improving the performance of graph neural networks
(GNNs) through enhancements in graph data, model architecture design, and training …

In real-world applications, machine learning models often become obsolete due to shifts in
the joint distribution arising from underlying temporal trends, a phenomenon known as the" …

Existing work on fairness modeling commonly assumes that sensitive attributes for all
instances are fully available, which may not be true in many real-world applications due to …

Defining fairness in algorithmic contexts is challenging, particularly when adapting to new
domains. Our research introduces a novel method for learning and applying group fairness …

Recent advances in fair graph learning observe that graph neural networks (GNNs) further
amplify prediction bias compared with multilayer perception (MLP), while the reason behind …

Deep reinforcement learning has recently achieved remarkable success in various domains,
ranging from games [10, 11, 12], to real-world applications such as neural architecture …

Deep learning on graphs has garnered considerable attention across various machine
learning applications, encompassing social science, transportation services, and biomedical …