Large language models (LLMs), exemplified by ChatGPT, have gained considerable
attention for their excellent natural language processing capabilities. Nonetheless, these …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Invariant graph representation learning aims to learn the invariance among data from
different environments for out-of-distribution generalization on graphs. As the graph …

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

Out-of-distribution (OOD) generalization deals with the prevalent learning scenario where
test distribution shifts from training distribution. With rising application demands and inherent …

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 …

Large language models (LLMs) have gained considerable attention for their excellent
natural language processing capabilities. Nonetheless, these LLMs present many …

This paper proposes a two-stage framework named ST-PAD for spatio-temporal fluid
dynamics modeling in the field of earth sciences, aiming to achieve high-precision …

We investigate the explainability of graph neural networks (GNNs) as a step toward
elucidating their working mechanisms. While most current methods focus on explaining …

Graph-based methods, pivotal for label inference over interconnected objects in many real-
world applications, often encounter generalization challenges, if the graph used for model …