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"author": "Wolfgang Gatterbauer",
"author_email": "gatt@cmu.edu",
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"Development Status :: 3 - Alpha",
"Intended Audience :: Developers",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
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"Programming Language :: Python",
"Programming Language :: Python :: 2",
"Programming Language :: Python :: 2.6",
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"description": "SSLH (Semi-Supervised Learning with Heterophily)\n================================================\n\n\nHome of ``SSLH`` on github:\n`http://github.com/sslh/sslh/ `__\n\n\nDocumentation\n-------------\n\nThis library implements efficient algorithms in linear algebra\nfor solving various inference and estimation problems\nin networks with observed heteorphily between classes of nodes (Heterophily: \"Opposites attract\" vs. Homophily: \"Birds of a feather flock together\").\nThe technical framework is that of undirected graphical models (Markov Random Fields or Markov Networks).\nThe key idea is that after applying certain linearization assumptions (that change the semantics) the resulting formulations\nallow several orders of magnitude speed-up in calculation.\n\nThe methods are described in detail in the following papers:\n\n1. `Linearized and Single-Pass Belief Propagation `__. `Wolfgang Gatterbauer `__, `Stephan G\u00fcnnemann `__, `Danai Koutra `__, `Christos Faloutsos `__. PVLDB 8(5): 581-592 (2015). [`Paper (PDF) `__], [`Full version (PDF) `__]\n\n2. `Semi-Supervised Learning with Heterophily `__. `Wolfgang Gatterbauer `__ [`Working paper (PDF) `__]\n\n\nUsage & Documentation\n---------------------\n\nThe package consists of:\n\n1. A directory ``sslh`` that contains files with the main methods\n\n2. A directory ``test`` that contains the test files, each of which makes use of methods from the corresonding file in the ``sslh`` directory.\n\nThus ideally take a look in the ``test`` directory, run some files and look through the annotations in the files.\n\n\nInstallation\n------------\n\nThe latest version of SSLH can be installed from the master branch using pip:\n\n.. code:: bash\n\n pip install sslh\n\nor\n\n.. code:: bash\n\n pip install git+https://github.com/wolfandthegang/sslh/\n\nAnother option is to clone the repository and install SSLH using ``python setup.py install`` or ``python setup.py develop``.\n\n\n\nDependencies\n------------\n\nSSLH is tested on Python 2.7 and depends on NumPy, SciPy, Sklearn, and PyAMG (see setup.py for version information).\n\n\nRelated initiatives\n-------------------\n\nSklearn: includes methods for semi-supervised learning (assuming homophily): http://scikit-learn.org/stable/modules/label_propagation.html\n\nPyPGMc: focusing on directed graphical models https://github.com/kadeng/pypgmc/\n\nOpenGM: implementation in C++ http://hci.iwr.uni-heidelberg.de/opengm2/, https://github.com/opengm/opengm\n\n\n--------------\n\nLicense\n-------\nCopyright 2015 Wolfgang Gatterbauer\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\n\nDistributed in the hope that it will be useful to other researchers,\nhowever, unless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License. You may obtain a copy of the License at\nhttp://www.apache.org/licenses/LICENSE-2.0\n\nContact Me\n----------\n\nQuestions or comments about ``SSLH``? Drop me an email at\ngatt@cmu.com.\n\n--------------\n\nChangelog\n=========\n\nVersion 0.1.0\n-------------\n\n- **Initial Release**: Main method 'linBP_undirected' for linearized belief propagation with one single doubly stochastic and symmetric potential as described in \"Linearized and Single-pass Belief Propagation\"",
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