From Facebook friend circles to hidden influencer groups, community detection in social networks is evolving fast. Researchers are combining deep learning, graph neural networks, and advanced ...

Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...

Their early AI analysis of Webb data identified a surprising number of a specific type of disc galaxies and added a new ...

A chair can still look like a chair even when its surface is reduced to a sparse cloud of points. Humans are remarkably good ...

Target identification is a critical and challenging step in drug discovery, with only a small fraction of human genes ...

Daisy chains are different. Their signal is structural. What matters is not one transaction, but the sequence. Imagine money ...

A study on visual language models explores how shared semantic frameworks improve image–text understanding across ...

Every year, the Earth shakes thousands of times. Most of those tremors go unnoticed, felt only by sensitive instruments buried deep in the ground. Occasionally, though, one of them tears apart cities, ...

Abstract: Dear Editor, This letter presents a novel graph neural network, namely modularized graph convolution network (MGCN), to address the underexplored issue in graph convolution networks (GCNs), ...

Abstract: Graph convolutional networks (GCNs) have attracted significant attention in the field of multi-view learning, as they effectively extract intricate information from diverse features.