Graph Convolutional Networks
Showing 9 of 9 projects
A PyTorch library for building and training Graph Neural Networks (GNNs) on structured and irregular data.
A comprehensive library for building and training Graph Neural Networks (GNNs) with PyTorch.
A curated collection of links to conference publications, surveys, and software in graph-based deep learning.
A Python library for machine learning on graphs and networks, offering state-of-the-art algorithms for tasks like node classification and link prediction.
A PyTorch library for spatiotemporal signal processing with dynamic and temporal graph neural networks.
A deep learning toolkit for computational chemistry and drug design research with PyTorch backend.
A deep learning library built on Chainer for molecular property prediction using graph convolutional neural networks.
A PyTorch implementation combining Graph Convolutional Networks with OpenNMT-py for structured data to text generation.
A PyTorch-based toolbox for graph reliability, focusing on adversarial attacks, defenses, and robustness techniques for graph neural networks.
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