AdaGCN
AdaGCN model
An implementation of an AdaGCN model that adapts Graph Convolutional Networks to deep models for graph neural networks tasks.
Official Implementation of AdaGCN (ICLR 2021)
60 stars
4 watching
12 forks
Language: Python
last commit: over 4 years agoLinked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
 benedekrozemberczki/appnp | A PyTorch implementation of a graph neural network model that learns personalized node representations | 367 |
 liqimai/gcn | An implementation of graph convolutional networks for semi-supervised learning in Python using TensorFlow and other libraries. | 45 |
| benedekrozemberczki/sgcn | An implementation of a deep learning algorithm for graph data | 270 |
 tensorflow/gnn | Builds Graph Neural Networks on the TensorFlow platform using heterogeneous graphs and various machine learning techniques. | 1,372 |
 snap-stanford/graphgym | A platform for designing and evaluating Graph Neural Networks (GNN) models | 1,738 |
| benedekrozemberczki/mixhop-and-n-gcn | A deep learning framework implementation of higher-order graph convolutional architectures and their applications | 403 |
 deepgraphlearning/gmnn | A software framework that integrates statistical relational learning and graph neural networks for semi-supervised object classification and unsupervised node representation learning. | 403 |
| benedekrozemberczki/capsgnn | A PyTorch implementation of a graph neural network architecture | 1,246 |
 hongyanggao/lgcn | An implementation of learnable graph convolutional networks for efficient graph processing | 46 |
 dawnranger/pytorch-agnn | An implementation of an attention-based graph neural network in PyTorch for semi-supervised learning | 146 |
 delta2323/gb-gnn | Analyzes and optimizes the performance of graph neural networks using gradient boosting and various aggregation models. | 13 |
 google-deepmind/jraph | A lightweight library for working with graph neural networks in jax. | 1,380 |
 bupt-gamma/openhgnn | An open-source toolkit for training and applying heterogeneous graph neural networks using PyTorch and the Deep Graph Library. | 879 |
| benedekrozemberczki/simgnn | An implementation of SimGNN, a neural network approach to computing graph similarity | 768 |
 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 |