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https://github.com/danielegrattarola/gnca
Code for "Learning Graph Cellular Automata" (NeurIPS 2021).
https://github.com/danielegrattarola/gnca
cellular-automata graph-neural-networks machine-learning neurips-2021 tensorflow
Last synced: 3 months ago
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Code for "Learning Graph Cellular Automata" (NeurIPS 2021).
- Host: GitHub
- URL: https://github.com/danielegrattarola/gnca
- Owner: danielegrattarola
- Created: 2021-10-15T12:58:48.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2022-09-16T11:58:05.000Z (over 2 years ago)
- Last Synced: 2024-11-02T02:50:45.937Z (3 months ago)
- Topics: cellular-automata, graph-neural-networks, machine-learning, neurips-2021, tensorflow
- Language: Python
- Homepage: https://arxiv.org/abs/2110.14237
- Size: 88.9 KB
- Stars: 60
- Watchers: 4
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Learning Graph Cellular Automata
This code implements the experiments from the NeurIPS 2021 paper:
**"Learning Graph Cellular Automata"**
_Daniele Grattarola, Lorenzo Livi, Cesare Alippi_
[Paper](https://arxiv.org/abs/2110.14237) |
[Blog](https://danielegrattarola.github.io/posts/2021-11-08/graph-neural-cellular-automata.html)
Bibtex:
```
@article{grattarola2021learning,
title={Learning graph cellular automata},
author={Grattarola, Daniele and Livi, Lorenzo and Alippi, Cesare},
journal={Advances in Neural Information Processing Systems},
volume={34},
pages={20983--20994},
year={2021}
}
```
## Setup
The dependencies of the project are listed in `requirements.txt`. You can install them with:
```sh
pip install -r requirements.txt
```
## Reproducing experiments
Most scripts have CLI options that you can use to control the behaviour.
Run:
```shell
python [script_name].py --help
```
to see a list of options.
### Voronoi GCA
The experiments with the Voronoi GCA can be reproduced using the scripts in the
`voronoi` folder.
To train the GNCA:
```shell
python run_voronoi.py
```
To compute the entropy of the GNCA after every training step:
```shell
python run_voronoi_entropy.py
```
To plot the entropies as a function of the rule's threshold:
```shell
python run_entropy_v_th.py
```
### Boids
The experiments with the Boids GCA can be reproduced using the scripts in the
`boids` folder.
To train the GNCA:
```shell
python run_boids.py
```
To compute the complexity of the GNCA every 10 training steps:
```shell
python run_boids.py --test_complexity_every 10
```
To make all the plots included in the paper, after training the GNCA with `run_boids.py`:
```shell
python evaluate_boids.py
```
To train the minimal MLP that implements the transition rule:
```shell
python run_learn_exact_mlp.py
```
### Fixed target
The experiments to train the GNCA to converge to a fixed target can be
reproduced using the scripts in the `fixed_target` folder.
To train the GNCA:
```shell
python run_fixed_target.py # By default, t=10
```
To train the GNCA by sampling t randomly in a range:
```shell
python run_fixed_target.py --min_steps 10 --max_steps 21 # t \in [10, 20]
```
To make all plots included in the paper:
```shell
python make_plots.py --path results/Grid2d/ # Replace with target folder for each graph
```