Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this ...

Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic graph node ...

Description The shader editor has a nice feature for vector outputs where you can click a little triangle and get the vector separated into its components without an extra node. 2025-06-30_22-38-08 ...

To address these challenges, we propose AffiGrapher, a physics-driven graph neural network that integrates a physics-informed graph architecture with contrastive learning. Incorporating multiple RNA ...