GB-GNN
Graph Neural Network Optimizer
Analyzes and optimizes the performance of graph neural networks using gradient boosting and various aggregation models.
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks
13 stars
3 watching
4 forks
Language: Jupyter Notebook
last commit: over 4 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
ivaniscoding/gnn-for-combinatorial-optimization | An implementation of graph neural networks for solving combinatorial optimization problems | 42 |
tensorflow/gnn | Builds Graph Neural Networks on the TensorFlow platform using heterogeneous graphs and various machine learning techniques. | 1,362 |
aqibsaeed/genetic-cnn | A tool for exploring and optimizing the architecture of Convolutional Neural Networks using a Genetic Algorithm | 218 |
ahmedfgad/neuralgenetic | Tools and techniques for training neural networks using genetic algorithms | 240 |
bupt-gamma/openhgnn | An open-source toolkit for training and applying heterogeneous graph neural networks using PyTorch and the Deep Graph Library. | 867 |
je-dbl/gnn-recsys | A framework for building and training Graph Neural Networks for recommendation systems | 277 |
benedekrozemberczki/mixhop-and-n-gcn | A deep learning framework implementation of higher-order graph convolutional architectures and their applications | 402 |
dawnranger/pytorch-agnn | An implementation of an attention-based graph neural network in PyTorch for semi-supervised learning | 145 |
gasteigerjo/ppnp | This project provides implementations of graph neural network models for personalized page rank task | 319 |
datake/adagcn | An implementation of a graph neural network technique to improve deep models | 60 |
stormraiser/gan-weight-norm | Improves the performance of Generative Adversarial Networks by normalizing weights and batch data | 181 |
sangyx/gtrick | A collection of reusable techniques to improve the performance of graph neural networks. | 284 |
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 |
benedekrozemberczki/sgcn | An implementation of a deep learning algorithm for graph data | 268 |
donggong1/learn-optimizer-rgdn | An implementation of deep learning-based optimization method for image deconvolution, which improves the quality of blurry images by generating new blur kernels. | 32 |