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CONVERT

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[ACM MM 2023] An official source code for paper "CONVERT: Contrastive Graph Clustering with Reliable Augmentation".

// repository documentation

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# CONVERT:Contrastive Graph Clustering with Reliable Augmentation

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An official source code for paper CONVERT:Contrastive Graph Clustering with Reliable Augmentation, accepted by ACM MM 23. Any communications or issues are welcomed. Please contact xihong_edu@163.com. If you find this repository useful to your research or work, it is really appreciate to star this repository. :heart:

-------------

### Overview

<p align = "justify"> 
 Illustration of CONVERT:Contrastive Graph Clustering with Reliable Augmentation mechanism. 
</p>
<div  align="center">    
    <img src="./assets/convert.jpg" width=60%/>
</div>

### Requirements

The proposed CONVERT is implemented with python 3.8.8 on a NVIDIA 2080 Ti GPU.

Python package information is summarized in **requirements.txt**:

- torch==1.8.0
- tqdm==4.61.2
- numpy==1.21.0
- tensorboard==2.8.0

### Quick Start

```
python train.py 
```

### Citation

If you use code or datasets in this repository for your research, please cite our paper.

```
@inproceedings{CONVERT,
  title={Convert: Contrastive graph clustering with reliable augmentation},
  author={Yang, Xihong and Tan, Cheng and Liu, Yue and Liang, Ke and Wang, Siwei and Zhou, Sihang and Xia, Jun and Li, Stan Z and Liu, Xinwang and Zhu, En},
  booktitle={Proceedings of the 31st ACM international conference on multimedia},
  pages={319--327},
  year={2023}
}
```

CONVERT

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