magnet
Graph neural network
A neural network designed to process directed graphs
MagNet graph convolutional network
33 stars
5 watching
6 forks
Language: Jupyter Notebook
last commit: 11 months ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 muhanzhang/dgcnn | A deep learning architecture for graph classification that extracts vertex features through propagation-based graph convolution and retains more node information than traditional sum pooling methods. | 174 |
 benedekrozemberczki/capsgnn | A PyTorch implementation of a graph neural network architecture | 1,246 |
 dawnranger/pytorch-agnn | An implementation of an attention-based graph neural network in PyTorch for semi-supervised learning | 145 |
 google-deepmind/jraph | A lightweight library for working with graph neural networks in jax. | 1,375 |
| benedekrozemberczki/appnp | A PyTorch implementation of a graph neural network model that learns personalized node representations | 363 |
 delta2323/gb-gnn | Analyzes and optimizes the performance of graph neural networks using gradient boosting and various aggregation models. | 13 |
 tensorflow/gnn | Builds Graph Neural Networks on the TensorFlow platform using heterogeneous graphs and various machine learning techniques. | 1,369 |
 gasteigerjo/ppnp | This project provides implementations of graph neural network models for personalized page rank task | 319 |
 magnet-dl/magnet | An API that simplifies the development of deep learning architectures by providing a high-level abstraction around PyTorch. | 360 |
 mims-harvard/ohmnet | An algorithm for learning feature representations in multi-layer networks | 79 |
 surenderthakran/gomind | A lightweight neural network library in Go | 84 |
 bupt-gamma/openhgnn | An open-source toolkit for training and applying heterogeneous graph neural networks using PyTorch and the Deep Graph Library. | 867 |
 deepgraphlearning/gmnn | A software framework that integrates statistical relational learning and graph neural networks for semi-supervised object classification and unsupervised node representation learning. | 401 |
 chingyaoc/ggnn.pytorch | An implementation of a neural network architecture for processing graph-structured data and making predictions on nodes. | 465 |
| benedekrozemberczki/sgcn | An implementation of a deep learning algorithm for graph data | 268 |