This repository provides a reference implementation of SDNE as described in the paper:

Structural Deep network Embedding.
Daixin Wang, Peng Cui, Wenwu Zhu
Knowledge Discovery and Data Mining, 2016.

The SDNE algorithm learns a representations for nodes in a graph. Please check the paper for more details.

$ python main.py -c config/xx.ini

noted: your can just checkout and modify config file or main.py to get what you want.

Your input graph data should be a txt file or a mat file and be under GraphData folder

The txt file should be edgelist and the first line should be N , the number of vertexes and E, the number of edges

The mat file should be the adjacent matrix.

you can save your adjacent matrix using the code below

import scipy.io as sio
sio.savemat("xxx.mat", {"graph_sparse":your_adjacent_matrix})

It is recommended to use mat file and save the adjacent matrix in a sparse form.

5242 14496
0 1
0 2
4 9
...
4525 4526

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 SDNE useful in your research, we ask that you cite the following paper:

@inproceedings{Wang:2016:SDN:2939672.2939753,
 author = {Wang, Daixin and Cui, Peng and Zhu, Wenwu},
 title = {Structural Deep Network Embedding},
 booktitle = {Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '16},
 year = {2016},
 isbn = {978-1-4503-4232-2},
 location = {San Francisco, California, USA},
 pages = {1225--1234},
 numpages = {10},
 url = {http:https://doi.acm.org/10.1145/2939672.2939753},
 doi = {10.1145/2939672.2939753},
 acmid = {2939753},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {deep learning, network analysis, network embedding},
}