Metadata-Version: 2.1
Name: linkpred
Version: 0.5.1
Summary: Python package for link prediction
Home-page: http://github.com/rafguns/linkpred/
Author: Raf Guns
Author-email: raf.guns@uantwerpen.be
License: New BSD License
Description: linkpred
        ========
        
        **linkpred** is a Python package for link prediction: given a network, **linkpred** provides a number of heuristics (known as *predictors*) that assess the likelihood of potential links in a future snapshot of the network.
        
        While some predictors are fairly straightforward (e.g., if two people have a large number of mutual friends, it seems likely that eventually they will meet and become friends), others are more involved.
        
        
        .. image:: https://travis-ci.org/rafguns/linkpred.svg?branch=master
            :target: https://travis-ci.org/rafguns/linkpred
        
        .. image:: https://coveralls.io/repos/rafguns/linkpred/badge.svg?branch=master
            :target: https://coveralls.io/r/rafguns/linkpred?branch=master
        
        
        **linkpred** can both be used as a command-line tool and as a Python library in your own code.
        
        
        Installation
        ------------
        
        **linkpred** (v0.5 and later) works under Python 3.6, 3.7, and 3.8. Version 0.4.1 was the last to support versions 3.4 and 3.5.
        It depends on:
        
        - matplotlib
        - networkx
        - numpy
        - pyyaml
        - scipy
        - smokesignal
        
        You should be able to install linkpred and its dependencies using pip (``pip install linkpred`` or ``python -m pip install linkpred``). If you do not yet have Python installed, I recommend starting with `Anaconda <https://www.continuum.io/downloads>`_, which includes optimized versions of packages like numpy. If you want to use the Community predictor, which relies on community structure of the network, make sure you also have the `python-louvain <https://github.com/taynaud/python-louvain>`_ package by installing with ``pip install linkpred[all]``.
        
        
        Example usage as command-line tool
        ----------------------------------
        
        A good starting point is ``linkpred --help``, which lists all the available options. To save the predictions of the ``CommonNeighbours`` predictor, for instance, run::
        
            $ linkpred examples/inf1990-2004.net -p CommonNeighbours --output cache-predictions
        
        where ``examples/inf1990-2004.net`` is a network file in Pajek format. Other supported formats include GML and GraphML. The full output looks like this:
        
        .. code:: console
        
            $ linkpred examples/inf1990-2004.net -p CommonNeighbours --output cache-predictions
            16:43:13 - INFO - Reading file 'examples/inf1990-2004.net'...
            16:43:13 - INFO - Successfully read file.
            16:43:13 - INFO - Starting preprocessing...
            16:43:13 - INFO - Removed 35 nodes (degree < 1)
            16:43:13 - INFO - Finished preprocessing.
            16:43:13 - INFO - Executing CommonNeighbours...
            16:43:14 - INFO - Finished executing CommonNeighbours.
            16:43:14 - INFO - Prediction run finished
        
            $ head examples/inf1990-2004-CommonNeighbours-predictions_2016-04-22_16.43.txt
            "Ikogami, K"    "Ikegami, K"    5.0
            "Durand, T"     "Abd El Kader, M"       5.0
            "Sharma, L"     "Kumar, S"      4.0
            "Paul, A"       "Durand, T"     4.0
            "Paul, A"       "Dudognon, G"   4.0
            "Paul, A"       "Abd El Kader, M"       4.0
            "Karisiddippa, CR"      "Garg, KC"      4.0
            "Wu, YS"        "Kretschmer, H" 3.0
            "Veugelers, R"  "Deleus, F"     3.0
            "Veugelers, R"  "Andries, P"    3.0
        
        
        Example usage within Python
        ---------------------------
        
        .. code:: pycon
        
            >>> import linkpred
            >>> G = linkpred.read_network("examples/training.net")
            11:49:00 - INFO - Reading file 'examples/training.net'...
            11:49:00 - INFO - Successfully read file.
            >>> len(G)   # number of nodes
            632
            >>> # We exclude edges already present, to predict only new links
            >>> simrank = linkpred.predictors.SimRank(G, excluded=G.edges())
            >>> simrank_results = simrank.predict(c=0.5)
            >>> top = simrank_results.top(5)
            >>> for authors, score in top.items():
            ...    print(authors, score)
            ...
            Tomizawa, H - Fujigaki, Y 0.188686630053
            Shirabe, M - Hayashi, T 0.143866427916
            Garfield, E - Fuseler, EA 0.148097050146
            Persson, O - Larsen, IM 0.138516589957
            Vanleeuwen, TN - Noyons, ECM 0.185040358711
        
Platform: any
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Development Status :: 4 - Beta
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Provides-Extra: community
