https://github.com/seijimaekawa/NAGC

Non-Linear Attributed Graph Clustering by Symmetric NMF with PU Learning
https://github.com/seijimaekawa/NAGC

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Non-Linear Attributed Graph Clustering by Symmetric NMF with PU Learning

• Host: GitHub
• URL: https://github.com/seijimaekawa/NAGC
• Owner: seijimaekawa
• Created: 2018-05-15T03:49:48.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-12-28T22:50:32.000Z (12 months ago)
• Last Synced: 2024-08-01T17:35:55.347Z (4 months ago)
• Language: Python
• Homepage:
• Size: 1.22 MB
• Stars: 8
• Watchers: 2
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NAGC

Preprint: [Non-Linear Attributed Graph Clustering by Symmetric NMF with PU Learning](https://arxiv.org/abs/1810.00946)

Journal: [New Attributed Graph Clustering by Bridging Attribute and Topology Spaces](https://www.jstage.jst.go.jp/article/ipsjjip/28/0/28_427/_pdf)

## Requirements

- numpy >= 1.15.1

- sklearn >= 0.19.1 (for kmeans and evaluation)

## Demo

- See the notebook NAGC_example.ipynb for demo

## References

If you find this repository useful, please consider giving a star and citing this work:

```bibtex

@article{maekawa2018nagc,

  title={Non-linear attributed graph clustering by symmetric NMF with PU learning},

  author={Maekawa, Seiji and Takeuch, Koh and Onizuka, Makoto},

  journal={arXiv preprint arXiv:1810.00946},

  year={2018}

}

@article{maekawa2020nagc,

  title={New Attributed Graph Clustering by Bridging Attribute and Topology Spaces},

  author={Maekawa, Seiji and Takeuchi, Koh and Onizuka, Makoto},

  journal={Journal of Information Processing},

  volume={28},

  pages={427--435},

  year={2020},

  publisher={Information Processing Society of Japan}

}

```