NAGC

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

Journal: New Attributed Graph Clustering by Bridging Attribute and Topology Spaces

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:

@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}
}