https://github.com/jwplayer/jwalk

:walking: Cython implementation of DeepWalk
https://github.com/jwplayer/jwalk

cython deep-learning deepwalk neural-network python

Last synced: about 1 year ago
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:walking: Cython implementation of DeepWalk

• Host: GitHub
• URL: https://github.com/jwplayer/jwalk
• Owner: jwplayer
• License: apache-2.0
• Created: 2016-12-06T15:38:56.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:09:07.000Z (almost 3 years ago)
• Last Synced: 2024-08-11T09:46:41.743Z (almost 2 years ago)
• Topics: cython, deep-learning, deepwalk, neural-network, python
• Language: Python
• Homepage: http://jwalk.readthedocs.io/en/latest/
• Size: 247 KB
• Stars: 54
• Watchers: 28
• Forks: 15
• Open Issues: 4
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.rst
  • License: LICENSE

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README

          
jwalk

=====

.. image:: https://travis-ci.org/jwplayer/jwalk.svg?branch=master

    :target: https://travis-ci.org/jwplayer/jwalk

    :alt: Build Status

.. image:: https://readthedocs.org/projects/jwalk/badge/?version=latest

    :target: http://jwalk.readthedocs.io/en/latest/?badge=latest

    :alt: Documentation Status

jwalk performs random walks on a graph and learns representations for nodes

using Word2Vec. It also has options to train existing models online and specify

weights.

Install

-------

::

    pip install -U jwalk

Build

-----

::

    make build

Usage

-----

::

    jwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' '

To see the full list of options:

::

    jwalk --help

    Prompt parameters:

      debug:            drop a debugger if an exception is raised

      delimiter:        delimiter for input file

      embedding-size:   dimension of word2vec embedding (default=200)

      has-header:       boolean if csv has header row

      help (-h):        argparse help

      input (-i):       file input (edgelist of 2/3 cols or adjacency matrix)

      log-level (-l)    logging level (default=INFO)

      model (-m):       use a pre-existing model

      num-walks (-n):   number of of random walks per graph (default=1)

      output (-o):      file output

      stats:            boolean to calculate walk statistics [requires pandas]

      undirected:       make graph undirected

      walk-length:      length of random walks (default=10)

      window-size:      word2vec window size (default=5)

      workers:          number of workers (default=multiprocessing.cpu_count)

Input File

~~~~~~~~~~

The input file can be of the following formats:

- Edgelist: CSV with 2 or 3 columns denoting the source, target and (optional)

  weight.

  There are CLI options to specify the delimiter and whether the file has

  a header (default=False).

  The CSV file is loaded using numpy if pandas is not installed. We strongly

  recommend using pandas to load the CSV as it's a lot faster.

- Graph: If the file has an extension that is ".npz", jwalk will assume

  that it is a `SciPy CSR matrix `_.

  Included must be keys of data, indices, indptr, shape and labels

  (default=None) where labels are the node labels.

  For an example, see tests/data/karate.npz.

Test

----

Running unit tests::

    make test

Running linter::

    make lint

Running tox::

    make test-all

Blog

----

Read more about jwalk in our blog post here:

https://www.jwplayer.com/blog/deepwalk-recommendations/

License

-------

Apache License 2.0

References

----------

- [paper]: arXiv:1403.6652  [cs.SI] "DeepWalk: Online Learning of Social Representations"

- [paper]: arXiv:1607.00653 [cs.SI] "node2vec: Scalable Feature Learning for Networks"