For a computationally more efficient approximation of this algorithm, which can also deal with graphs of different sizes, check out the code here: https://github.com/Hermina/fGOT,

accompanying our AAAI 2022 paper "fGOT: Graph Distances based on Filters and Optimal Transport".

fGOT paper link: https://arxiv.org/pdf/2109.04442.pdf

GOT

This is the code for the Neurips 2019 paper GOT: An Optimal Transport framework for Graph comparison

Paper link: https://papers.nips.cc/paper/9539-got-an-optimal-transport-framework-for-graph-comparison.pdf

If you find this code useful in your research, please cite:

@incollection{NIPS2019_9539,
title = {GOT: An Optimal Transport framework for Graph comparison},
author = {Petric Maretic, Hermina and El Gheche, Mireille and Chierchia, Giovanni and Frossard, Pascal},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {13876--13887},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/9539-got-an-optimal-transport-framework-for-graph-comparison.pdf}
}