https://github.com/atong01/trainable_symmetry

Implementation of learnable generalized geometric scattering transforms on graphs
https://github.com/atong01/trainable_symmetry

gnn graph-neural-networks scattering-networks scattering-transform

Last synced: 3 days ago
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Implementation of learnable generalized geometric scattering transforms on graphs

• Host: GitHub
• URL: https://github.com/atong01/trainable_symmetry
• Owner: atong01
• Created: 2022-08-22T14:39:50.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-03T04:05:46.000Z (almost 2 years ago)
• Last Synced: 2024-11-01T02:02:27.723Z (about 2 months ago)
• Topics: gnn, graph-neural-networks, scattering-networks, scattering-transform
• Language: Python
• Homepage:
• Size: 858 KB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        


# Trainable Symmetry










[![Paper](http://img.shields.io/badge/paper-arxiv.1911.06253-B31B1B.svg)](https://arxiv.org/abs/1911.06253)



This is code for the paper \`\`Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms''. For tables presented in the paper see `notebooks/results_eval.ipynb`.

## Description

This code implements a generalized geometric scattering transform implemented in pytorch and pytorch lightning and configured by hydra.

## How to run

Install dependencies

```bash

# clone project

git clone https://github.com/atong01/trainable_symmetry

cd trainable_symmetry

# [OPTIONAL] create conda environment

conda create -n myenv python=3.9

conda activate myenv

# install pytorch according to instructions

# https://pytorch.org/get-started/

# install requirements

pip install -r requirements.txt

```

Copy `.env.example` to `.env` and configure directories in `.env` as needed.

To reproduce experiments in paper (also in `scripts/basic.sh`):

```bash

python src/train.py -m datamodule.transform_args.alpha=-0.5,-0.25,0.0,0.25,0.5 \

  datamodule.dataset=NCI1,NCI109,DD,PROTEINS,MUTAG,PTC_MR,REDDIT-BINARY,REDDIT-MULTI-5K,COLLAB,IMDB-BINARY,IMDB-MULTI \

  logger=wandb \

  datamodule.transform_args.power=1,2 \

  seed=0,1,2,3,4,5,6,7,8,9

python src/train.py -m datamodule.transform_args.alpha=-0.5,-0.25,0.0,0.25,0.5 \

  datamodule.dataset=NCI1,NCI109,DD,PROTEINS,MUTAG,PTC_MR,REDDIT-BINARY,REDDIT-MULTI-5K,COLLAB,IMDB-BINARY,IMDB-MULTI \

  logger=wandb \

  datamodule.transform_args.power=1 \

  +datamodule.transform_args.cheb_order=10,100\

  seed=0,1,2,3,4,5,6,7,8,9

```

```bash

# train on CPU

python src/train.py trainer=cpu

# train on GPU

python src/train.py trainer=gpu

```

You can override any parameter from command line like this

```bash

python src/train.py trainer.max_epochs=20

```

# BibTex Citation

```

@misc{perlmutter_understanding_2019,

  doi = {10.48550/ARXIV.1911.06253},

  url = {https://arxiv.org/abs/1911.06253},

  author = {Perlmutter, Michael and Gao, Feng and Wolf, Guy and Hirn, Matthew},

  keywords = {Machine Learning (stat.ML), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms},

  publisher = {arXiv},

  year = {2019},

}

```