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DeepWalk - Deep Learning for Graphs

Project README

=============================== 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

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

Evaluation

Here, we will show how to evaluate DeepWalk on the BlogCatalog dataset used in the DeepWalk paper. First, we run the following command to produce its DeepWalk embeddings::

deepwalk --format mat --input example_graphs/blogcatalog.mat
--max-memory-data-size 0 --number-walks 80 --representation-size 128 --walk-length 40 --window-size 10
--workers 1 --output example_graphs/blogcatalog.embeddings

The parameters specified here are the same as in the paper. If you are using a multi-core machine, try to set --workers to a larger number for faster training. On a single machine with 24 Xeon E5-2620 @ 2.00GHz CPUs, this command takes about 20 minutes to finish (--workers is set to 20). Then, we evaluate the learned embeddings on a multi-label node classification task with example_graphs/scoring.py::

python example_graphs/scoring.py --emb example_graphs/blogcatalog.embeddings
--network example_graphs/blogcatalog.mat
--num-shuffle 10 --all

This command finishes in 8 minutes on the same machine. For faster evaluation, you can set --num-shuffle to a smaller number, but expect more fluctuation in performance. The micro F1 and macro F1 scores we get with different ratio of labeled nodes are as follows:

+-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ | % Labeled Nodes | 10% | 20% | 30% | 40% | 50% | 60% | 70% | 80% | 90% | +=================+=======+=======+=======+=======+=======+=======+=======+=======+=======+ | Micro-F1 (%) | 35.86 | 38.51 | 39.96 | 40.76 | 41.51 | 41.85 | 42.27 | 42.35 | 42.40 | +-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+ | Macro-F1 (%) | 21.08 | 23.98 | 25.71 | 26.73 | 27.68 | 28.28 | 28.88 | 28.70 | 28.21 | +-----------------+-------+-------+-------+-------+-------+-------+-------+-------+-------+

Note that the current version of DeepWalk is based on a newer version of gensim, which may have a different implementation of the word2vec model. To completely reproduce the results in our paper, you will probably have to install an older version of gensim(version 0.10.2).

Requirements

  • numpy
  • scipy

(may have to be independently installed) or pip install -r requirements.txt to install all dependencies

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.

  • Free software: GPLv3 license

.. 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

Open Source Agenda is not affiliated with "Deepwalk" Project. README Source: phanein/deepwalk
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