DeepWalk

DeepWalk uses short random walks to learn representations for vertices in graphs.

Usage

Example Usage: $deepwalk --input example_graphs/karate.adjlist --output karate.embeddings

--input: input_filename

--format adjlist for an adjacency list, e.g:
1 2 3 4 5 6 7 8 9 11 12 13 14 18 20 22 32
2 1 3 4 8 14 18 20 22 31
3 1 2 4 8 9 10 14 28 29 33
...
--format edgelist for an edge list, e.g:
1 2
1 3
1 4
...
--format mat for a Matlab MAT file containing an adjacency matrix

(note, you must also specify the variable name of the adjacency matrix --matfile-variable-name)

--output: output_filename

The output representations in skipgram format - first line is header, all other lines are node-id and d dimensional representation:
34 64
1 0.016579 -0.033659 0.342167 -0.046998 ...
2 -0.007003 0.265891 -0.351422 0.043923 ...
...

Full Command List: The full list of command line options is available with $deepwalk --help

Requirements

numpy
scipy

(may have to be independently installed)

Installation

cd deepwalk
pip install -r requirements.txt
python setup.py install

Citing

If you find DeepWalk useful in your research, we ask that you cite the following paper:

@inproceedings{Perozzi:2014:DOL:2623330.2623732,
 author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
 title = {DeepWalk: Online Learning of Social Representations},
 booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '14},
 year = {2014},
 isbn = {978-1-4503-2956-9},
 location = {New York, New York, USA},
 pages = {701--710},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2623330.2623732},
 doi = {10.1145/2623330.2623732},
 acmid = {2623732},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},
}

Misc

DeepWalk - Online learning of social representations.