Efficient Subgraph GNNs by Learning Effective Selection Policies

This repository contains the official code of the paper Efficient Subgraph GNNs by Learning Effective Selection Policies (ICLR 2024).

Reproduce results

To perform hyperparameter tuning, make use of wandb:

1. In the yaml-files folder, choose the yaml file corresponding to the dataset of interest, say <config-name>. This file contains the hyperparameters grid.

2. Run

   wandb sweep yaml-files/<config-name>

   to obtain a sweep id <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:

   • Compute mean and std of best val, test metric @ best val by grouping over all hyperparameters and averaging over the different seeds. Then, take the results corresponding to the configuration obtaining the best validation metric.

Credits

For attribution in academic contexts, please cite

@inproceedings{bevilacqua2024efficient,
title={Efficient {S}ubgraph {GNN}s by Learning Effective Selection Policies},
author={Beatrice Bevilacqua and Moshe Eliasof and Eli Meirom and Bruno Ribeiro and Haggai Maron},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
}