Metadata-Version: 1.1
Name: deepwalk
Version: 1.0.3
Summary: DeepWalk online learning of social representations.
Home-page: https://github.com/phanein/deepwalk
Author: Bryan Perozzi
Author-email: bperozzi@cs.stonybrook.edu
License: GPLv3
Description: ===============================
        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
        
        
        
        
        History
        -------
        
        1.0.3 (2018-03-23)
        ---------------------
        
        * Now compatible with the latest version of gensim and sklearn
        * Better support for Python 3
        
        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.
        
Keywords: deepwalk
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
