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توضیحات

DeepWalk online learning of social representations.

ویژگی	مقدار
سیستم عامل	-
نام فایل	deepwalk-v1.0.2
نام	deepwalk
نسخه کتابخانه	v1.0.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Bryan Perozzi
ایمیل نویسنده	bperozzi@cs.stonybrook.edu
آدرس صفحه اصلی	https://github.com/phanein/deepwalk
آدرس اینترنتی	https://pypi.org/project/deepwalk/
مجوز	GPLv3

=============================== 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) Installation ------------ #. cd deepwalk #. pip install -r requirements.txt #. 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 * Documentation: http://deepwalk.readthedocs.org. .. 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.

نیازمندی

مقدار	نام
>=0.23.0	wheel
>=0.20.2	Cython
>=1.2.1	argparse
>=2.1.6	futures
>=1.7.3	six
>=1.0.0	gensim
>=0.15.0	scipy
>=2.1.1	psutil

نحوه نصب

نصب پکیج whl deepwalk-v1.0.2:

pip install deepwalk-v1.0.2.whl

نصب پکیج tar.gz deepwalk-v1.0.2:

pip install deepwalk-v1.0.2.tar.gz