sdne's Introduction

SDNE

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.

Basic Usage

$ python main.py

After execute the above command, You will get a embedding file named "xxx_embedding.mat"

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

Input

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

file format

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

A sample

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 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://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},
}

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