{ "info": { "author": "Anton Tsitsulin", "author_email": "anton.tsitsulin@hpi.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "===============================\nNetLSD\n===============================\n\nNetLSD is a family of spectral graph descriptros. Given a graph, NetLSD computes a low-dimensional vector representation that can be used for different tasks.\n\nQuick start\n-----------\n\n.. code-block:: python\n\n import netlsd\n import networkx as nx\n\n g = nx.erdos_renyi_graph(100, 0.01) # create a random graph with 100 nodes\n descriptor = netlsd.heat(g) # compute the signature\n\nThat's it! Then, signatures of two graphs can be compared easily. NetLSD supports `networkx `_, `graph_tool `_, and `igraph `_ packages natively.\n\n.. code-block:: python\n\n import netlsd\n import numpy as np\n\n distance = netlsd.compare(desc1, desc2) # compare the signatures using l2 distance\n distance = np.linalg.norm(desc1 - desc2) # equivalent\n\n\nFor more advanced usage, check out `online documentation `_.\n\n\nRequirements\n------------\n* numpy\n* scipy\n\n\nInstallation\n------------\n#. cd netlsd\n#. pip install -r requirements.txt \n#. python setup.py install\n\nOr simply ``pip install netlsd``\n\nCiting\n------\nIf you find NetLSD useful in your research, we ask that you cite the following paper::\n\n @inproceedings{Tsitsulin:2018:KDD,\n author={Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Bronstein, Alex and M{\\\"u}ller, Emmanuel},\n title={NetLSD: Hearing the Shape of a Graph},\n booktitle = {Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},\n series = {KDD '18},\n year = {2018},\n } \n\nMisc\n----\n\nNetLSD - Hearing the shape of graphs.\n\n* MIT license\n* Documentation: http://netlsd.readthedocs.org", "description_content_type": "", "docs_url": null, "download_url": "https://github.com/xgfs/netlsd/archive/0.1.tar.gz", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "http://github.com/xgfs/netlsd", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "NetLSD", "package_url": "https://pypi.org/project/NetLSD/", "platform": "", "project_url": "https://pypi.org/project/NetLSD/", "project_urls": { "Download": "https://github.com/xgfs/netlsd/archive/0.1.tar.gz", "Homepage": "http://github.com/xgfs/netlsd" }, "release_url": "https://pypi.org/project/NetLSD/1.0.2/", "requires_dist": null, "requires_python": "", "summary": "NetLSD descriptors for graphs. 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