Published as a conference paper at ICLR 2024 as a spotplight paper.

1. We present a class of maximum mean discrepancy (MMD) based graph kernels, called MMD-GK. These kernels are computed by applying MMD to the node representations of two graphs with message-passing propagation.
2. Based on this vanilla version, we provide a class of deep MMD-GKs that are able to learn graph kernels and implicit graph features adaptively in an unsupervised manner.
3. Apart from that, we propose a class of supervised deep MMD-GKs that are able to utilize label information of graphs and hence yield more discriminative metrics.

Remember to install all the dependencies as below.

pip install -r requirements.txt

We provide a sample dataset (MUTAG) in the data folder. Please configure your settings in utils/arguments.py

Run the vanilla version (MMDGK) with a command:

python main.py --model 'vanilla'

Run the deep version (Deep MMDGK) with a command:

python main.py --model 'deep'

@inproceedings{sun2023mmd,
  title={MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy},
  author={Sun, Yan and Fan, Jicong},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2023}
}