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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

  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

Requirements

  • numpy
  • scipy

(may have to be independently installed)

Installation

  1. cd deepwalk
  2. pip install -r requirements.txt
  3. 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.

History

1.0.0 (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.
Release History

Release History

1.0.1

This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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1.0.0

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v1.0.2

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
deepwalk-1.0.1.tar.gz (29.1 kB) Copy SHA256 Checksum SHA256 Source Sep 19, 2014

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