Awesome

ggme: <u>G</u>raph <u>G</u>enerative <u>M</u>odel <u>E</u>valuation

This is the official repository for the ICLR 2022 paper "Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions" https://openreview.net/forum?id=tBtoZYKd9n

Dependencies

Dependencies are managed using poetry. To setup the environment, please run poetry install from the main directory (assuming the user already has installed poetry).

Running ggme

The primary script is contained in main.py. We assume that the user has two distributions which they would like to compare using MMD, given a specified kernel and descriptor function.

We assume that each distribution of graphs is stored as a list of networkx graphs.

Example script

We provide an example run in main.py based on predictions of a graph generative model and the graphs in the corresponding test set. To run this, execute the following code from the main directory.

cd src
poetry run python main.py

Citing our work

Please consider citing our work:

@inproceedings{obray2022evaluation,
	title        = {Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutions},
	author       = {Leslie O'Bray and Max Horn and Bastian Rieck and Karsten Borgwardt},
	year         = 2022,
	booktitle    = {International Conference on Learning Representations},
	url          = {https://openreview.net/forum?id=tBtoZYKd9n}
}