ESAN
ESAN copied to clipboard
Equivariant Subgraph Aggregation Networks (ICLR 2022 Spotlight)
Equivariant Subgraph Aggregation Networks (ESAN)
This repository contains the official code of the paper Equivariant Subgraph Aggregation Networks (ICLR 2022)
Install
First create a conda environment
conda env create -f environment.yml
and activate it
conda activate subgraph
Prepare the data
Run
python data.py --dataset $DATASET
where $DATASET
is one of the following:
- MUTAG
- PTC
- PROTEINS
- NCI1
- NCI109
- IMDB-BINARY
- IMDB-MULTI
- ogbg-molhiv
- ogbg-moltox21
- ZINC
- CSL
- EXP
- CEXP
Run the models
To perform hyperparameter tuning, make use of wandb
:
-
In
configs/
folder, choose theyaml
file corresponding to the dataset and setting (deterministic vs sampling) of interest, say<config-name>
. This file contains the hyperparameters grid. -
Run
wandb sweep configs/<config-name>
to obtain a sweep id
<sweep-id>
-
Run the hyperparameter tuning with
wandb agent <sweep-id>
You can run the above command multiple times on each machine you would like to contribute to the grid-search
-
Open your project in your wandb account on the browser to see the results:
-
For the TUDatasets, the CSL and the EXP/CEXP datasets, refer to
Metric/valid_mean
andMetric/valid_std
to obtain the results. -
For the ogbg datasets and the ZINC dataset, compute mean and std of
Metric/train_mean
,Metric/valid_mean
,Metric/test_mean
over the different seeds of the same configuration. Then, take the results corresponding to the configuration obtaining the best validation metric.
-
Credits
For attribution in academic contexts, please cite
@inproceedings{bevilacqua2022equivariant,
title={Equivariant Subgraph Aggregation Networks},
author={Beatrice Bevilacqua and Fabrizio Frasca and Derek Lim and Balasubramaniam Srinivasan and Chen Cai and Gopinath Balamurugan and Michael M. Bronstein and Haggai Maron},
booktitle={International Conference on Learning Representations},
year={2022},
}