Skip to content

Latest commit

 

History

History
32 lines (27 loc) · 1.22 KB

README.md

File metadata and controls

32 lines (27 loc) · 1.22 KB
Raw

ImVerde

An implementation for "ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation" (Big Data'18). [Paper] [arXiv]

Environment Requirements

The code is adapted from [Revisiting Semi-Supervised Learning with Graph Embeddings]. It has been tested under Python 3.6.5. The required packages are listed as follows:

  • numpy == 1.17.3
  • scipy == 1.3.1
  • sklearn ==0.21.3
  • Lasagne == 0.2.dev1
  • Theano == 1.0.4

Data sets

We used three public data sets in our experiments: Cora, Citeseer, Pubmed.

Run the Codes

python runMe.py

Acknowledgement

This is the latest source code of ImVerde for BigData2018. If you find that it is helpful for your research, please consider to cite our paper:

@inproceedings{wu2018imverde,
  title={ImVerde: Vertex-diminished random walk for learning imbalanced network representation},
  author={Wu, Jun and He, Jingrui and Liu, Yongming},
  booktitle={2018 IEEE International Conference on Big Data (Big Data)},
  pages={871--880},
  year={2018},
  organization={IEEE}
}