Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

The field of drug discovery has experienced a remarkable transformation with the advent of
artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and …

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

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

Although graph neural networks have achieved great success in the task of molecular
property prediction in recent years, their generalization ability under out-of-distribution …

Link prediction is a fundamental problem for network-structured data and has achieved
unprecedented success in many real-world applications. Despite the significant progress …

Graph-structured data plays a foundational role in knowledge representation across various
intelligent systems. Self-supervised graph representation learning (SSGRL) has emerged as …

Landslide susceptibility assessment (LSA) is vital for landslide mitigation and management.
Existing LSA methods only consider local environmental characteristics associated with …