This repository provides a reference implementation of HSRL as described in the paper.
$ 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.
Your input graph data should be a txt file and be under data folder.
The txt file should be edgelist.
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.
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}
}