Classic Graph Convolutional Networks (GCNs) often learn node representation holistically, which would ignore the distinct impacts from different neighbors when aggregating their features to update ...

News Release 4-Feb-2024 Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks Peer-Reviewed Publication Higher Education Press image: Framework of DTT-STG view more ...

Models of dynamic networks—networks that evolve over time—have manifold applications. We develop a discrete time generative model for social network evolution that inherits the richness and ...

GNNs extend the foundational ideas of Convolutional Neural Networks (CNNs) to graph data. While CNNs capture spatial locality in grid-like data (for example, images) through convolutional kernels, ...

“Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks.” arXiv preprint arXiv:2309.06423 (2023). Related Reading Journey From Cell-Aware To Device-Aware Testing Begins Better ...