Abstract Out-of-distribution (OOD) generalization is an important issue for Graph Neural
Networks (GNNs). Recent works employ different graph editions to generate augmented …

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-
related tasks. However, the black-box nature often limits their interpretability and …

Graph Neural Networks (GNNs) have received increasing attention due to their ability to
learn from graph-structured data. However, their predictions are often not interpretable. Post …

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

Deep graph learning (DGL) has achieved remarkable progress in both business and
scientific areas ranging from finance and e-commerce, to drug and advanced material …

The growth in social media has exacerbated the threat of fake news to individuals and
communities. This draws increasing attention to developing efficient and timely rumor …

Spatio-temporal graph neural networks (STGNNs) have gained popularity as a powerful tool
for effectively modeling spatio-temporal dependencies in diverse real-world urban …

Despite the recent progress of molecular representation learning, its effectiveness is
assumed on the close-world assumptions that training and testing graphs are from identical …

Graph serves as a powerful tool for modeling data that has an underlying structure in non-
Euclidean space, by encoding relations as edges and entities as nodes. Despite …

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 …