This repository contains the official code of the paper Equivariant Subgraph Aggregation Networks (ICLR 2022)

Related tags

Deep Learning subgraph graph-neural-networks invariance equivariance message-passing-neural-network
Overview

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.

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:

  1. In configs/ folder, choose the yaml file corresponding to the dataset and setting (deterministic vs sampling) of interest, say . This file contains the hyperparameters grid.

  2. Run

    wandb sweep configs/<config-name>

    to obtain a sweep id

  3. 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

  4. 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 and Metric/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},
}
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Comments
  • Issue with data edge_attr is None

    Issue with data edge_attr is None

    Hello, Thanks for releasing the code.

    I am getting an error in a few datasets (IMDB-B, PROTEINS) about edge_attr being None, as soon as this object is needed in the convolution. Was this issue encountered before? or any possible suggestions?

    Thanks

    opened by dgm2 4
Owner
Beatrice Bevilacqua
Beatrice Bevilacqua
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