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 …

Abstract Graph Contrastive Learning (GCL) has shown superior performance in
representation learning in graph-structured data. Despite their success, most existing GCL …

Graph Neural Networks (GNNs) have shown remarkable performance in various tasks.
However, recent works reveal that GNNs are vulnerable to backdoor attacks. Generally …

Graph neural networks have shown great ability in representation (GNNs) learning on
graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent …

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

Fairness in graph neural networks has been actively studied recently. However, existing
works often do not explicitly consider the role of message passing in introducing or …

Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions
neither favoring nor harming certain groups defined by sensitive attributes (eg, race and …

Graph representation learning has shown superior performance in numerous real-world
applications, such as finance and social networks. Nevertheless, most existing works might …

Despite the remarkable capabilities demonstrated by Graph Neural Networks (GNNs) in
graph-related tasks, recent research has revealed the fairness vulnerabilities in GNNs when …

In an increasingly connected world, graph mining plays a fundamental role in many real-
world applications, such as financial fraud detection, drug discovery, traffic prediction, and …