Accurate graph classification via two-staged contrastive curriculum learning

This repository is a Pytorch implementation of Accurate graph classification via two-staged contrastive curriculum learning, submitted to PLOS ONE 2023.

Settings

We implement codes based on the following environments:

• Python 3.7
• PyTorch 1.4.0
• PyTorch Geometric 1.6.3

Datasets

We use seven datasets for graph classification. All datasets can be downloaded from link. If you run the code, the datasets will be downloaded in the dataset/ folder. The detailed information on datasets is summarized in the table below.

Name	Graphs	Nodes	Edges	Features	Classes
MUTAG	188	3371	3721	7	2
PROTEINS	1113	43471	81044	3	2
NCI1	4110	122747	132753	37	2
NCI109	4127	122494	132604	38	2
DD	1178	334925	843046	89	2
PTC_MR	344	4915	5054	18	2
DBLP	19456	203954	764512	41325	2

Code Information

We explain codes for TAG (Two-staged contrAstive curriculum learning for Graphs) and describe how to run the code. main.py gets hyperparameters and conducts the overall process of TAG. The proposed method is implemented in model/tag.py and proposed augmentation algorithms are implemented inside the augment folder.

To run this project, you have to type the following command.

python main.py

This command runs the code in the default setting.