Frontiers

Multimodal graph neural networks in healthcare: a review of fusion strategies across ...

1 Department of Analytics, Harrisburg University of Science and Technology, Harrisburg, PA, United States 2 Department of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States ...
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 ...
IEEE

Identification of Depression with a Semi-supervised GCN based on EEG Data

Abstract: In this paper, to explore the application of depression EEG data in semi-supervised classification, we designed an improved semi-supervised graph convolutional neural network model for ...
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

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 ...