Frontiers

Multi-Scale graph wavelet convolutional network for hyperspectral image classification

Hyperspectral images (HSIs) have very high dimensionality and typically lack sufficient labeled samples, which significantly challenges their processing and analysis. These challenges contribute to ...
Frontiers

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

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 ...
Scientific Research Publishing

Kipf, T.N. and Welling, M. (2016) Semi-Supervised Classification with Graph Convolutional ...

ABSTRACT: The rapid advancements in large language models (LLMs) have led to an exponential increase in survey papers, making it challenging to systematically track and analyze their evolving taxonomy ...
IEEE

Semi-Supervised Learning With Graph Learning-Convolutional Networks

Abstract: Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing graph CNNs generally use a fixed ...
IEEE

Directed Adaptive Multi-Channel Graph Convolutional Network

Abstract: Compared with traditional neural networks, graph convolutional networks are very suitable for processing graph structured data. However, common graph convolutional network methods often have ...
Scientific Research Publishing

Semi-Supervised Stochastic Configuration Networks Based on Manifold Regularization Framework ()

1 School of Mathematics and Statistics, Guilin University of Technology, Guilin, China. 2 Guangxi Colleges and Universities Key Laboratory of Applied Statistics, Guilin, China. The stochastic ...