DeepWalk online learning of social representations.
Project description
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.
Free software: GPLv3 license
Documentation: http://deepwalk.readthedocs.org.
History
1.0.2 (2014-09-19)
Fixed gensim at 0.10.2 for now
1.0.1 (2014-09-19)
Added utilities to support generated embeddings for larger graphs
Support for additional input file formats
1.0.0 (2014-08-24)
First release on PyPI.
Project details
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