Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …

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

Deep graph neural networks (GNNs) have achieved excellent results on various tasks on
increasingly large graph datasets with millions of nodes and edges. However, memory …

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so
that research on out-of-distribution (OOD) generalization comes into the spotlight …

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 …

Graph neural networks (GNNs) integrate deep architectures and topological structure
modeling in an effective way. However, the performance of existing GNNs would decrease …

The interdependence between nodes in graphs is key to improve class prediction on nodes,
utilized in approaches like Label Probagation (LP) or in Graph Neural Networks (GNNs) …

Graph convolutional networks (GCNs) have received significant attention from various
research fields due to the excellent performance in learning graph representations. Although …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …