OpenHGNN
by BUPT-GAMMA
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
AI summary
Graph Neural Network Toolkit
An open-source toolkit for training and applying heterogeneous graph neural networks using PyTorch and the Deep Graph Library.
- stars
- 879
- forks
- 146
- watching
- 10
Similar projects
Found by comparing what the projects do, not just their names.
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
tensorflow/gnn1.4K
Graph Network Library
Builds Graph Neural Networks on the TensorFlow platform using heterogeneous graphs and various machine learning techniques.
Deep learning toolkit
A Python framework for building deep learning models with optimized encoding layers and batch normalization.
Federated GNN
An implementation of a federated graph neural network for spatio-temporal modeling
Graph Neural Network Model
A PyTorch implementation of a graph neural network model that learns personalized node representations
Graph learning framework
A software framework that integrates statistical relational learning and graph neural networks for semi-supervised object classification and unsupervised node representation learning.
Graph neural network
A PyTorch implementation of a graph neural network architecture
Graph Neural Network
An implementation of an attention-based graph neural network in PyTorch for semi-supervised learning
Graph processing neural net
An implementation of a neural network for graph data, specifically designed to process wavelet transforms on graphs.
Graph neural network library
A PyTorch Geometric extension library for working with signed and directed graphs
Graph processor
An implementation of learnable graph convolutional networks for efficient graph processing
GQN model
A PyTorch implementation of a Generative Query Network model for generating 3D scenes and rendering them in various styles.
Graph classifier
Implementations of a graph neural network model for personalized graph classification
Graph Neural Network Optimizer
Analyzes and optimizes the performance of graph neural networks using gradient boosting and various aggregation models.