MIT License

项目介绍

Shen-Lab/GraphCL 是一个托管在 GitHub 上的开源项目,主要使用 Python 编写,采用 MIT License 许可证,目前获得 636 个 Star、108 次 Fork。

仓库全名
Shen-Lab/GraphCL
开源许可证
MIT License
Star 数
636
Fork 数
108
未关闭 Issue
33

README 摘要

Graph Contrastive Learning with Augmentations PyTorch implementation for Graph Contrastive Learning with Augmentations [[poster]](https://yyou1996.github.io/files/neurips2020graphclposter.pdf) [[appendix]](https://yyou1996.github.io/files/neurips2020graphclsupplement.pdf) Yuning You \ , Tianlong Chen \ , Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen In NeurIPS 2020. External links For the automated version of…

完整内容见 GitHub 原始 README 或本页 。

常见问题

GraphCL 用什么语言开发?

主要使用 Python 编写,采用 MIT License 许可证,目前有 636 个 Star。

GraphCL 可以免费商用吗?

仓库采用 MIT License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 GraphCL 的源码?

可以直接克隆仓库:git clone https://github.com/Shen-Lab/GraphCL.git,默认分支为 master;安装与运行步骤见仓库 README。

GraphCL 最近还在维护吗?

仓库最近一次更新为 2024年7月17日,当前有 33 个未关闭 Issue;Star 数为 636,可作为社区活跃度的参考。

关于

[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
861
价值分
413
活跃状态
活跃

语言占比

C
Cuda
Makefile
Python
Shell

README

Graph Contrastive Learning with Augmentations

PyTorch implementation for Graph Contrastive Learning with Augmentations [poster] [appendix]

Yuning You*, Tianlong Chen*, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen

In NeurIPS 2020.

For the automated version of GraphCL, please refer to https://github.com/Shen-Lab/GraphCL_Automated.

For the extention of GraphCL to hypergraphs, please refer to https://github.com/weitianxin/HyperGCL.

For the most comprehensive collection of graph SSL papers, please refer to https://github.com/ChandlerBang/awesome-self-supervised-gnn.

Overview

In this repository, we develop contrastive learning with augmentations for GNN pre-training (GraphCL, Figure 1) to address the challenge of data heterogeneity in graphs. Systematic study is performed as shown in Figure 2, to assess the performance of contrasting different augmentations on various types of datasets.

Experiments

Potential Issues

Some issues might occur due to the version mismatch. I collect them as follows (keep updating).

Citation

If you use this code for you research, please cite our paper.

@inproceedings{You2020GraphCL,
 author = {You, Yuning and Chen, Tianlong and Sui, Yongduo and Chen, Ting and Wang, Zhangyang and Shen, Yang},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
 pages = {5812--5823},
 publisher = {Curran Associates, Inc.},
 title = {Graph Contrastive Learning with Augmentations},
 url = {https://proceedings.neurips.cc/paper/2020/file/3fe230348e9a12c13120749e3f9fa4cd-Paper.pdf},
 volume = {33},
 year = {2020}
}

评论

暂无评论