Graph Mixup on Approximate Gromov–Wasserstein Geodesics

Overview

Implementation of Graph Mixup on Approximate Gromov–Wasserstein Geodesics in ICML 2024

Environment Setup

    conda create -n geomix python=3.12
    conda activate geomix
    conda install pytorch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 pytorch-cuda=12.1 -c pytorch -c nvidia
    pip install torch_geometric
    conda install -c conda-forge pot
    conda install matplotlib

Run the code

    python src/train.py --data MUTAG --model GCN --num_node 20 --augment True

• --data: select from IMDB-BINARY | IMDB-MULTI | MUTAG | PROTEINS | MSRC_9.
• --model: select from GCN | GIN | APPNP.
• --num_node: size of the mixup graph (20 for IMDB/MUTAG, 40 for PROTEINS/MSRC_9).
• --augment: True to perform GeoMix, False to use backbone vanilla models.

Dataset

Dataset	# Graphs	# Nodes	# Edges	# Features	# Class
PROTEINS	1,113	43.31	77.79	1	2
MUTAG	188	17.93	19.79	None	2
MSRC-9	221	40.58	97.94	None	8
IMDB-B	1,000	19.77	96.53	None	2
IMDB-M	1,500	12.74	53.88	None	3

Mixup graph visualization

Reference

If you find this paper helpful to your research, please kindly cite the following paper:

@InProceedings{pmlr-v235-zeng24e,
  title = 	 {Graph Mixup on Approximate Gromov–{W}asserstein Geodesics},
  author =       {Zeng, Zhichen and Qiu, Ruizhong and Xu, Zhe and Liu, Zhining and Yan, Yuchen and Wei, Tianxin and Ying, Lei and He, Jingrui and Tong, Hanghang},
  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
  pages = 	 {58387--58406},
  year = 	 {2024},
  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
  volume = 	 {235},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {21--27 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/zeng24e/zeng24e.pdf},
  url = 	 {https://proceedings.mlr.press/v235/zeng24e.html},
  abstract = 	 {Mixup, which generates synthetic training samples on the data manifold, has been shown to be highly effective in augmenting Euclidean data. However, finding a proper data manifold for graph data is non-trivial, as graphs are non-Euclidean data in disparate spaces. Though efforts have been made, most of the existing graph mixup methods neglect the intrinsic geodesic guarantee, thereby generating inconsistent sample-label pairs. To address this issue, we propose GeoMix to mixup graphs on the Gromov-Wasserstein (GW) geodesics. A joint space over input graphs is first defined based on the GW distance, and graphs are then transformed into the GW space through equivalence-preserving transformations. We further show that the linear interpolation of the transformed graph pairs defines a geodesic connecting the original pairs on the GW manifold, hence ensuring the consistency between generated samples and labels. An accelerated mixup algorithm on the approximate low-dimensional GW manifold is further proposed. Extensive experiments show that the proposed GeoMix promotes the generalization and robustness of GNN models.}
}