Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

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 …

Recent studies show that the predictive performance of graph neural networks (GNNs) is
inconsistent and varies across different experimental runs, even with identical parameters …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications such as …

The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have
gained significant attention and demonstrated remarkable performance in various domains …

Generating molecules with high binding affinities to target proteins (aka structure-based
drug design) is a fundamental and challenging task in drug discovery. Recently, deep …

Graph Neural Networks (GNNs) have achieved promising results in various tasks such as
node classification and graph classification. Recent studies find that GNNs are vulnerable to …

Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks,
where attackers embed hidden backdoors in the DNN model by injecting a few poisoned …