IEEE

Multigranularity Deep Graph Convolutional Neural Network Node Clustering Leveraging Spatial Information

Abstract: In the era of information explosion, clustering analysis of graph-structured data and empty graph-structured data is of great significance for extracting the intrinsic value of data. From ...
Frontiers

Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances

Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal ...
Sports Illustrated

Massive spike in college football viewership across all major networks

The business of college football is booming so far in 2025, at least according to the TV ratings through the opening weeks of the season. Front Office Sports reported that total college football ...
C&EN

Integrating ESM-2 and Graph Neural Networks with AlphaFold-2 Structures for Enhanced Protein Function Prediction

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Protein function prediction is essential for elucidating biological processes and ...
IEEE

Multiscale Gaussian Process-Driven Graph Convolutional Neural Network for Polarimetric SAR Image Classification

Abstract: By focusing on the structure exploration and information propagation from non-Euclidean data space, graph convolutional neural network (GCN), which can extract abundant and discriminative ...
Frontiers

Association prediction of lncRNAs and diseases using multiview graph convolution neural network

1 College of Computer Science and Engineering, Changsha University, Changsha, Hunan, China 2 Department of Information and Computing Science, College of Mathematics, Changsha University, Changsha, ...