IEEE

Semi-Supervised Learning of Multi-Scale Graph Neural Networks for Industrial Process Fault ...

Abstract: The scarcity of labeled data is a critical challenge in industrial process multi-scale modeling, as learning reliable models from limited labeled data and large-scale unlabeled data is ...
Scientific Research Publishing

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

ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...
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 ...