This project is a PyTorch implementation of "AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification" (PAKDD 2025). The paper proposes AugWard, a novel graph representation framework that carefully considers the diversity introduced by graph augmentation.

Our implementation is based on Python and PyTorch Geometric.

• Python 3.9.12
• PyTorch 2.1.0
• PyTorch Geometric 2.4.0

We include requirements.txt, which contains all the packages used for the experiment. We checked the dependency using a workstation with CUDA 11.6 installed. Install the required packages with the following code:

pip install -r requirements.txt

We include run.sh, which reproduces the graph classification result on PTC_MR dataset.

bash run.sh

To run with different settings, change the arguments passed into src/run.py. Check the terminal and src/results/ folder for the results.

We utilize 10 datasets in our work on graph classification task, which are all from the Datasets Source. The datasets could be easily downloaded using PyTorch Geometric.

Note: The values for Nodes and Edges are average values per each graph instance.

This repository is written based on the codes from "Model-Agnostic Augmentation for Accurate Graph Classification" (WWW '22) [GitHub].

You can download this bib file or copy the following information:

@inproceedings{KimCLJK25,
    title={AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification},
    author={Kim, Minjun and Choi, Jaehyeon and Lee, Seungjoo and Jung, Jinhong and Kang, U},
    booktitle={The 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining},
    year={2025}
}