IEEE

FSL-EGNN: Edge-Labeling Graph Neural Network for Hyperspectral Image Few-Shot Classification

Abstract: The existing hyperspectral image (HSI) classification encounters the obstacle of improving the classification accuracy with limited labeled samples. In this context, as a typical ...
GitHub

Graph Neural Networks for Tabular Data Learning (GNN4TDL)

The deep learning-based approaches to Tabular Data Learning (TDL), classification and regression, have shown competing performance, compared to their conventional counterparts. However, the latent ...
GitHub

CEGANN: Crystal Edge Graph Attention Neural Network

Implementation of Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to perform multiscale classification of materials. Copy CEGAN code in the ...
C&EN

Topology-Based Phase Identification of Bulk, Interface, and Confined Water Using an Edge-Conditioned Convolutional Graph Neural Network

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
IEEE

Graph Neural Network via Edge Convolution for Hyperspectral Image Classification

Abstract: Graph neural network (GNN) has recently gained increasing attention in the hyperspectral image (HSI) classification. Compared with convolutional neural network (CNN), GNN can effectively ...
Frontiers

Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data ...
Oak Ridge National Lab

Architectures and Performance group

At Oak Ridge National Laboratory, the Computing and Computational Sciences Directorate’s (CCSD’s) Architectures and Performance group is transforming how scientists harness the power of supercomputers ...