# GraphCL

**Repository Path**: r1Way/graph-cl

## Basic Information

- **Project Name**: GraphCL
- **Description**: 对抑郁症数据使用GraphCL
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-22
- **Last Updated**: 2025-11-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 项目说明

本仓库基于以下项目进行开发和融合：
- [Shen-Lab/GraphCL](https://github.com/Shen-Lab/GraphCL): [NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
- [facebookresearch/mixup-cifar10](https://github.com/facebookresearch/mixup-cifar10): mixup: Beyond Empirical Risk Minimization

我们在 OpenNeuro 数据集（Resting state with closed eyes for patients with depression and healthy participants）上进行了测试和实验。

## Dependencies

* [PyTorch Geometric](https://github.com/rusty1s/pytorch_geometric#installation)==1.6.0

Then, you need to create a directory for recoreding finetuned results to avoid errors:

```
mkdir logs
```

## Training & Evaluation

```
./go.sh $GPU_ID $DATASET_NAME $AUGMENTATION
```

```$DATASET_NAME``` is the dataset name (please refer to https://chrsmrrs.github.io/datasets/docs/datasets/), ```$GPU_ID``` is the lanched GPU ID and ```$AUGMENTATION``` could be ```random2, random3, random4``` that sampling from {NodeDrop, Subgraph}, {NodeDrop, Subgraph, EdgePert} and {NodeDrop, Subgraph, EdgePert, AttrMask}, seperately.

## Acknowledgements

The backbone implementation is reference to https://github.com/fanyun-sun/InfoGraph/tree/master/unsupervised.