guoji-fu / hsrl

Learning Topological Representation for Networks via Hierarchical Sampling

HSRL

This repository provides a reference implementation of HSRL as described in the paper.

Basic Usage

$ python hsrl.py --input 'data/movielens/train_edges.txt' --output 'output/movielens/movielens_dw_hs_lp_embeddings.txt' --method deepwalk

noted: your can just checkout hsrl.py to get what you want.

Input

Your input graph data should be a txt file and be under data folder.

file format

The txt file should be edgelist.

txt file sample

0 163
0 359
0 414
...
5297 4973

noted: The nodeID start from 0.
noted: The graph should be an undirected graph, so if (I J) exist in the Input file, (J I) should not.

Citing

If you find HSRL useful in your research, please cite our paper:

@inproceedings{fu2019learning,
  title={Learning topological representation for networks via hierarchical sampling},
  author={Fu, Guoji and Hou, Chengbin and Yao, Xin},
  booktitle={2019 International Joint Conference on Neural Networks (IJCNN)},
  pages={1--8},
  year={2019},
  organization={IEEE}
}

hsrl's Issues

Question about how to evaluate it

Hello,professor,could you give me the evaluation function? thanks!

something about the datasets

Hello.professor. Do you have the processed datasets with weight?