{ "info": { "author": "Kamil Sindi, Nir Yungster", "author_email": "kamil@jwplayer.com, nir@jwplayer.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: Apache Software License", "Programming Language :: Cython", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering" ], "description": "jwalk\n=====\n\n.. image:: https://travis-ci.org/jwplayer/jwalk.svg?branch=master\n :target: https://travis-ci.org/jwplayer/jwalk\n :alt: Build Status\n\n.. image:: https://readthedocs.org/projects/jwalk/badge/?version=latest\n :target: http://jwalk.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\njwalk performs random walks on a graph and learns representations for nodes\nusing Word2Vec. It also has options to train existing models online and specify\nweights.\n\nInstall\n-------\n\n::\n\n pip install -U jwalk\n\nBuild\n-----\n\n::\n\n make build\n\nUsage\n-----\n\n::\n\n jwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' '\n\nTo see the full list of options:\n\n::\n\n jwalk --help\n\n Prompt parameters:\n debug: drop a debugger if an exception is raised\n delimiter: delimiter for input file\n embedding-size: dimension of word2vec embedding (default=200)\n has-header: boolean if csv has header row\n help (-h): argparse help\n input (-i): file input (edgelist of 2/3 cols or adjacency matrix)\n log-level (-l) logging level (default=INFO)\n model (-m): use a pre-existing model\n num-walks (-n): number of of random walks per graph (default=1)\n output (-o): file output\n stats: boolean to calculate walk statistics [requires pandas]\n undirected: make graph undirected\n walk-length: length of random walks (default=10)\n window-size: word2vec window size (default=5)\n workers: number of workers (default=multiprocessing.cpu_count)\n\n\nInput File\n~~~~~~~~~~\n\nThe input file can be of the following formats:\n\n- Edgelist: CSV with 2 or 3 columns denoting the source, target and (optional)\n weight.\n There are CLI options to specify the delimiter and whether the file has\n a header (default=False).\n The CSV file is loaded using numpy if pandas is not installed. We strongly\n recommend using pandas to load the CSV as it's a lot faster.\n\n- Graph: If the file has an extension that is \".npz\", jwalk will assume\n that it is a `SciPy CSR matrix `_.\n Included must be keys of data, indices, indptr, shape and labels\n (default=None) where labels are the node labels.\n For an example, see tests/data/karate.npz.\n\n\nTest\n----\n\nRunning unit tests::\n\n make test\n\nRunning linter::\n\n make lint\n\nRunning tox::\n\n make test-all\n\nBlog\n----\nRead more about jwalk in our blog post here:\nhttps://www.jwplayer.com/blog/deepwalk-recommendations/\n\nLicense\n-------\n\nApache License 2.0\n\nReferences\n----------\n\n- [paper]: arXiv:1403.6652 [cs.SI] \"DeepWalk: Online Learning of Social Representations\"\n- [paper]: arXiv:1607.00653 [cs.SI] \"node2vec: Scalable Feature Learning for Networks\"", "description_content_type": null, "docs_url": null, "download_url": "UNKNOWN", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/jwplayer/jwalk", "keywords": "deep learning,neural networks,deepwalk", "license": "Apache License 2.0", "maintainer": null, "maintainer_email": null, "name": "jwalk", "package_url": "https://pypi.org/project/jwalk/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/jwalk/", "project_urls": { "Download": "UNKNOWN", "Homepage": "https://github.com/jwplayer/jwalk" }, "release_url": "https://pypi.org/project/jwalk/0.5.3/", "requires_dist": null, "requires_python": null, "summary": "Representational learning on graphs", "version": "0.5.3", "yanked": false, "yanked_reason": null }, "last_serial": 8783474, "releases": { "0.5.0": [ { "comment_text": "", "digests": { "md5": "096ca0d803456ef7349b8dd45447bd64", "sha256": "89ae62b4b877f236f2d1acd9efb14394ad7ba3bc22a795883dceccd276b04eaa" }, "downloads": -1, "filename": "jwalk-0.5.0.tar.gz", "has_sig": false, "md5_digest": "096ca0d803456ef7349b8dd45447bd64", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 349432, "upload_time": "2017-01-10T22:55:44", "upload_time_iso_8601": "2017-01-10T22:55:44.270009Z", "url": "https://files.pythonhosted.org/packages/5b/c2/7cb71b61bfde4200afbcaeebcf146121c78f7ff62fb76fde9e893e4fc79e/jwalk-0.5.0.tar.gz", "yanked": false, "yanked_reason": null } ], "0.5.3": [ { "comment_text": "", "digests": { "md5": "856d832490495f0ae287f7d475ec990f", "sha256": "ac6440544241f3e6000b053119a22a7d0d90c443ae468702d2ea7e065e3762a7" }, "downloads": -1, "filename": "jwalk-0.5.3.tar.gz", "has_sig": false, "md5_digest": "856d832490495f0ae287f7d475ec990f", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 355911, "upload_time": "2017-04-24T12:44:50", "upload_time_iso_8601": "2017-04-24T12:44:50.530132Z", "url": "https://files.pythonhosted.org/packages/5a/ba/4bcecb790787ac81898daded7c32df06631aae5797107a463289fcbf0fd8/jwalk-0.5.3.tar.gz", "yanked": false, "yanked_reason": null } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "856d832490495f0ae287f7d475ec990f", "sha256": "ac6440544241f3e6000b053119a22a7d0d90c443ae468702d2ea7e065e3762a7" }, "downloads": -1, "filename": "jwalk-0.5.3.tar.gz", "has_sig": false, "md5_digest": "856d832490495f0ae287f7d475ec990f", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 355911, "upload_time": "2017-04-24T12:44:50", "upload_time_iso_8601": "2017-04-24T12:44:50.530132Z", "url": "https://files.pythonhosted.org/packages/5a/ba/4bcecb790787ac81898daded7c32df06631aae5797107a463289fcbf0fd8/jwalk-0.5.3.tar.gz", "yanked": false, "yanked_reason": null } ], "vulnerabilities": [] }