{"id":18367678,"url":"https://github.com/jwplayer/jwalk","last_synced_at":"2025-04-06T16:32:53.934Z","repository":{"id":15174965,"uuid":"75745341","full_name":"jwplayer/jwalk","owner":"jwplayer","description":":walking: Cython implementation of DeepWalk","archived":false,"fork":false,"pushed_at":"2023-07-06T21:09:07.000Z","size":253,"stargazers_count":54,"open_issues_count":4,"forks_count":15,"subscribers_count":28,"default_branch":"master","last_synced_at":"2024-08-11T09:46:41.743Z","etag":null,"topics":["cython","deep-learning","deepwalk","neural-network","python"],"latest_commit_sha":null,"homepage":"http://jwalk.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jwplayer.png","metadata":{"files":{"readme":"README.rst","changelog":"CHANGELOG.rst","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-12-06T15:38:56.000Z","updated_at":"2024-05-10T03:43:08.000Z","dependencies_parsed_at":"2022-09-03T03:24:13.590Z","dependency_job_id":null,"html_url":"https://github.com/jwplayer/jwalk","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwplayer%2Fjwalk","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwplayer%2Fjwalk/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwplayer%2Fjwalk/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jwplayer%2Fjwalk/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jwplayer","download_url":"https://codeload.github.com/jwplayer/jwalk/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223257931,"owners_count":17115000,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cython","deep-learning","deepwalk","neural-network","python"],"created_at":"2024-11-05T23:22:53.882Z","updated_at":"2024-11-05T23:22:54.431Z","avatar_url":"https://github.com/jwplayer.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"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 \u003chttps://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.sparse.csr_matrix.html\u003e`_.\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\"\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjwplayer%2Fjwalk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjwplayer%2Fjwalk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjwplayer%2Fjwalk/lists"}