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https://github.com/xbresson/spatial_graph_convnets
PyTorch implementation of residual gated graph ConvNets, ICLR’18
https://github.com/xbresson/spatial_graph_convnets
Last synced: 19 days ago
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PyTorch implementation of residual gated graph ConvNets, ICLR’18
- Host: GitHub
- URL: https://github.com/xbresson/spatial_graph_convnets
- Owner: xbresson
- License: mit
- Created: 2018-04-24T05:56:57.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-24T09:10:20.000Z (about 6 years ago)
- Last Synced: 2024-08-01T17:25:41.144Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 4.1 MB
- Stars: 121
- Watchers: 5
- Forks: 34
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-graph-classification - [Python Pytorch Reference
README
# Residual Gated Graph ConvNets
April 24, 2018
### Xavier Bresson
http://www.ntu.edu.sg/home/xbresson
https://github.com/xbresson
https://twitter.com/xbresson
https://www.facebook.com/xavier.bresson.1
### Description
Prototype implementation in PyTorch of the ICLR'18 paper:
An Experimental Study of Neural Networks for Variable Graphs
Xavier Bresson and Thomas Laurent
International Conference on Learning Representations, 2018
ICLR OpenReview: https://openreview.net/pdf?id=SJexcZc8G
ArXiv extended version: [arXiv:1711.07553](https://arxiv.org/pdf/1711.07553v2.pdf)
[ICLR Poster]
[ICLR Poster]: poster/poster_ICLR18.pdf
### Codes
The code `01_residual_gated_graph_convnets_subgraph_matching.ipynb` presents an application of the residual gated graph convNets for the problem of sub-graph matching.
The code `02_residual_gated_graph_convnets_semisupervised_clustering.ipynb` shows another application for the problem of semi-supervised_clustering.
### Installation
```sh
# Conda installation
curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh # Linux
curl -o ~/miniconda.sh -O https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh # OSX
chmod +x ~/miniconda.sh
./miniconda.sh
source ~/.bashrc
# Clone GitHub repo
git clone https://github.com/xbresson/spatial_graph_convnets.git
cd spatial_graph_convnets
# Install python libraries
conda env create -f environment.yml
conda activate graph_convnets
# Run the 2 notebooks
jupyter notebook
```
### Results
GeForce GTX 1080Ti
* Sub-graph matching: **01_residual_gated_graph_convnets_subgraph_matching.ipynb**, accuracy= 98.85.
* Semi-supervised_clustering: **02_residual_gated_graph_convnets_semisupervised_clustering.ipynb**, accuracy= 75.88.
### When to use this algorithm?
Any problem that can be cast as analyzing a set of graphs with variable size and connectivity, and one wants to use ConvNets for this analysis.