Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/phanein/deepwalk

DeepWalk - Deep Learning for Graphs
https://github.com/phanein/deepwalk

Last synced: 25 days ago
JSON representation

DeepWalk - Deep Learning for Graphs

Awesome Lists containing this 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