$deepwalk --help
input: adjacency list
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
...
output: representations in skipgram format - first line is header, all other lines are node-id and representation
34 64
1 0.016579 -0.033659 0.342167 -0.046998 ...
2 -0.007003 0.265891 -0.351422 0.043923 ...
...
- numpy
- scipy
(may have to be independently installed)
- cd deepwalk
- pip install -r requirements.txt
- python setup.py install
@inproceedings{2014-perozzi-deepwalk,
author = {Bryan Perozzi and Rami Al-Rfou and Steven Skiena},
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},
month = {August},
location = {New York, NY, USA},
publisher = {ACM},
address = {New York, NY, USA},
}
DeepWalk - Online learning of social representations.
- Free software: GPLv3 license
- Documentation: http://deepwalk.readthedocs.org.
