pygcn
by tkipf
Graph Convolutional Networks in PyTorch
AI summary
GCN model
A PyTorch implementation of a graph neural network architecture for semi-supervised classification
- stars
- 5.2K
- forks
- 1.2K
- watching
- 55
Similar projects
Found by comparing what the projects do, not just their names.
Graph clustering library
A PyTorch implementation of a clustering algorithm for graph neural networks
diego999/pygat2.9K
Graph Network Model
An implementation of the Graph Attention Network model using PyTorch.
Graph neural network library
A PyTorch-based library for training and applying Graph Neural Networks to structured data
Graph neural network
A PyTorch implementation of a graph neural network architecture
Graph algorithm
An implementation of a deep learning algorithm for graph data
Graph classifier
A PyTorch implementation of a semi-supervised graph classification model that learns hierarchical representations from labeled and unlabeled graph data.
Graph CNN
A deep learning framework implementation of higher-order graph convolutional architectures and their applications
Graph neural network library
A Python library for building graph neural networks with Keras and TensorFlow 2.
Graph neural network
An implementation of a neural network architecture for processing graph-structured data and making predictions on nodes.
Temporal graph NN library
A PyTorch extension for building temporal graph neural networks with support for recurrent and attention-based models
locuslab/tcn4.2K
Sequence modeler
An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Deep Learning Tutorials
A comprehensive tutorial project that provides code examples for learning PyTorch by implementing various deep learning models and demonstrating their usage.
Graph Neural Network Model
A PyTorch implementation of a graph neural network model that learns personalized node representations
GANs
Replication of various Generative Adversarial Networks (GANs) and their variants in PyTorch
Graph processing neural net
An implementation of a neural network for graph data, specifically designed to process wavelet transforms on graphs.