junwu6 / MARINE

Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy

MARINE (MAnifold-RegularIzed Network Embedding)

An implementation for "Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy" (CIKM'19). [Paper]

Environment Requirements

The code has been tested under Python 3.6.5. The required packages are as follows:

• tensorflow == 1.12.0
• numpy == 1.15.4
• scipy == 1.1.0
• sklearn == 0.20.0
• networkx == 2.3

Data sets

We used four data sets in our experiments: Cora, Citeseer, Pubmed and Wiki.

Run the Codes

python main.py

Acknowledgement

This is the latest source code of MARINE for CIKM2019. If you find that it is helpful for your research, please consider to cite our paper:

@inproceedings{wu2019scalable,
  title={Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy},
  author={Wu, Jun and He, Jingrui},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={2101--2104},
  year={2019},
  organization={ACM}
}