We provide the code (in pytorch) and datasets for our paper "Learning to Count Isomorphisms with Graph Neural Networks", which is accepted by AAAI23.

The repository is organised as follows:

• datasets/data/: contains data we use. Need to be decompressed and be placed in the same path as Count_GNN/
• Count_GNN/: contains our model.
• converter/: transform the original dataset into the data format that can be inputted into Count_GNN.
• generator/: generate synthetic dataset.
• Appendix.pdf: Appendix of our paper "Learning to Count Isomorphisms with Graph Neural Networks"

• tqdm
• numpy
• pandas
• scipy
• tensorboardX
• torch >= 1.3.0
• dgl == 0.4.3post2

• train model: python _train.py --model EDGEMEAN --predict_net FilmSumPredictNet --emb_dim 4 --ppn_hidden_dim 12 --weight_decay_film 0.0001
• test model: python evaluate.py ../dumps/MUTAG

@inproceedings{yu2023learning,
title={Learning to Count Isomorphisms with Graph Neural Networks},
author={Yu, Xingtong and Liu, Zemin and Fang, Yuan and Zhang, Xinming},
booktitle={Proceedings of the AAAI conference on artificial intelligence},
year={2023}
}