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 …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

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 …

Machine learning models are becoming pervasive in high-stakes applications. Despite their
clear benefits in terms of performance, the models could show discrimination against …

Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …

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 …

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

Graph neural networks (GNNs) have demonstrated remarkable success in various real-
world applications. However, they often inadvertently inherit and amplify existing societal …

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

Despite recent advances in achieving fair representations and predictions through
regularization, adversarial debiasing, and contrastive learning in graph neural networks …