This repository is the official implementation of Graph Alignment Kernels using Weisfeiler and Leman Hierarchies.

Code is written in Python 3.6 and requires:

• NetworkX 2
• sklearn 0.23

Download the datasets from here: https://chrsmrrs.github.io/datasets/docs/datasets/ and then extract them into the "datasets" folder

To create the kernel matrix and then perform 10-fold cross validation, first specify the dataset and the hyperparameter T in the main.py file and then execute:

python main.py

Please cite our paper if you use this code:

@inproceedings{nikolentzos2023graph,
  title={Graph Alignment Kernels using Weisfeiler and Leman Hierarchies},
  author={Nikolentzos, Giannis and Vazirgiannis, Michalis},
  booktitle={Proceedings of the 26th International Conference on Artificial Intelligence and Statistics},
  pages={2019--2034},
  year={2023}
}