IEEE

GCN-RP: Graph Convolutional Network-Based Predictor for Network Connectivity Resilience

Abstract: Evaluating the connectivity resilience of real-world networks through attack simulations is a time-consuming process. This study proposes a method that trains Graph Convolutional Networks ...
GitHub

Spatial Temporal Graph Convolutional Networks (ST-GCN)

Our demo for skeleton based action recognition: ST-GCN is able to exploit local pattern and correlation from human skeletons. Below figures show the neural response magnitude of each node in the last ...
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 ...
IEEE

LDA-GCN: Localized Differential Attention for Graph Convolutional Network

Abstract: Graph Convolutional Networks (GCNs) have demonstrated significant efficacy in graph representation learning. Existing approaches often rely on stacking multiple GCN layers to incorporate ...
C&EN

GGCRB: A Graph Neural Network Approach for Predicting CircRNA–RBP Interactions Using ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The interaction between circular RNAs (circRNAs) and RNA-binding proteins (RBPs) plays ...
C&EN

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case ...

Department of Chemistry and Biochemistry, University of Wisconsin─Eau Claire, Eau Claire, Wisconsin 54702, United States ...