DeepWalk by Brian Perozzi on Theano to enable distributed training of embedding on multiple GPUs. Also, entire code uses Keras under the hood in order to build flexible versions of models And as soon as Keras now support TensorFlow as a backend you can train your embedding of graphs using this Google product.
Also in plans:
- Using graphlets to augment Deepwalk-based embeddings using multimodal side information ( using Siamese networks )
- Multimodal graph embedding ( text,graphics,graph topology etc )
DeepWalk uses short random walks to learn representations for vertices in graphs.
Example Usage
$deepwalk --input example_graphs/karate.adjlist --output karate.embeddings --window-size=15 --representation-size=128 --workers=8 --number-walks=80
--input: input_filename
1. ``--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
...
2. ``--format edgelist`` for an edge list, e.g::
1 2
1 3
1 4
...
3. ``--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
- numpy
- scipy
- Theano
(may have to be independently installed)
#. cd deepwalk #. pip install -r requirements.txt #. python setup.py install
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},
}
DeepWalk - Online learning of social representations.
- Free software: GPLv3 license
- Documentation: http://deepwalk.readthedocs.org.
.. image:: https://badge.fury.io/py/deepwalk.png :target: http://badge.fury.io/py/deepwalk
.. image:: https://travis-ci.org/phanein/deepwalk.png?branch=master :target: https://travis-ci.org/phanein/deepwalk
.. image:: https://pypip.in/d/deepwalk/badge.png :target: https://pypi.python.org/pypi/deepwalk Stay tuned!
