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 neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

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 …

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

Graph Neural Networks (GNNs) have demonstrated a great representation learning
capability on graph data and have been utilized in various downstream applications …

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

As Large Language Models (LLMs) are increasingly deployed to handle various natural
language processing (NLP) tasks, concerns regarding the potential negative societal …

Abstract Graph Neural Networks (GNNs) have shown satisfying performance on various
graph learning tasks. To achieve better fitting capability, most GNNs are with a large number …

Achieving microscopy with large space-bandwidth products plays a key role in diagnostic
imaging and is widely significant in the overall field of clinical practice. Among quantitative …

Fairness is a key issue in many real decision-making applications. Existing Graph Neural
Network (GNN) models, designed for making non-discrimination decisions, are dependent …