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CONVERT:Contrastive Graph Clustering with Reliable Augmentation
<p align="center"> <a href="https://pytorch.org/" alt="PyTorch"> <img src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?e&logo=PyTorch&logoColor=white" /></a> <a href="https://www.acmmm2023.org" alt="Conference"> <img src="https://img.shields.io/badge/ACM MM'23-brightgreen" /></a> <p/>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 31th ACM International Conference on Multimedia},
pages={},
year={2023}
}