Metadata-Version: 1.2
Name: pathpy2
Version: 2.0.0
Summary: An OpenSource python package for the analysis and visualisation of time series data oncomplex networks with higher- and multi-order graphical models.
Home-page: https://www.pathpy.net
Author: Ingo Scholtes
Author-email: scholtes@ifi.uzh.ch
License: AGPL-3.0+
Description: Introduction
        ============
        
        ``pathpy`` is an OpenSource python package for the analysis of time
        series data on networks using **higher-order** and **multi-order**
        graphical models.
        
        The package is specifically tailored to analyze temporal networks as
        well as sequential data that capture multiple short, independent paths
        observed in an underlying graph topology. Examples for data that can be
        analysed with ``pathpy`` include time-stamped social networks, user
        click streams in information networks, biological pathways, or traces of
        information propagating in social media. Unifying the analysis of
        pathways and temporal networks, ``pathpy`` provides various methods to
        extract time-respecting paths from time-stamped network data. It extends
        (and will eventually supersede) the package
        ```pyTempnets`` <https://github.com/IngoScholtes/pyTempNets>`__.
        
        ``pathpy`` facilitates the analysis of temporal correlations in time
        series data on networks. It uses a principled model selection technique
        to infer higher-order graphical representations that capture both
        topological and temporal characteristics. It specifically allows to
        answer the question when a network abstraction of time series data is
        justified and when higher-order network representations are needed.
        
        The theoretical foundation of this package, **higher-order network
        models**, was developed in the following research works:
        
        1. I Scholtes: `When is a network a network? Multi-Order Graphical Model
           Selection in Pathways and Temporal
           Networks <http://dl.acm.org/citation.cfm?id=3098145>`__, In KDD'17 -
           Proceedings of the 23rd ACM SIGKDD International Conference on
           Knowledge Discovery and Data Mining, Halifax, Nova Scotia, Canada,
           August 13-17, 2017
        2. I Scholtes, N Wider, A Garas: `Higher-Order Aggregate Networks in the
           Analysis of Temporal Networks: Path structures and
           centralities <http://dx.doi.org/10.1140/epjb/e2016-60663-0>`__, The
           European Physical Journal B, 89:61, March 2016
        3. I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer:
           `Causality-driven slow-down and speed-up of diffusion in
           non-Markovian temporal
           networks <http://www.nature.com/ncomms/2014/140924/ncomms6024/full/ncomms6024.html>`__,
           Nature Communications, 5, September 2014
        4. R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer:
           `Betweenness preference: Quantifying correlations in the topological
           dynamics of temporal
           networks <http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.198701>`__,
           Phys Rev Lett, 110(19), 198701, May 2013
        
        ``pathpy`` extends this approach towards **multi-layer graphical
        models** that capture temporal correlations at multiple length scales
        simultaneously. An illustrative example for a collection of paths (left)
        and a multi-order graphical representation is shown below. All
        mathematical details of the framework can be found in this `recent
        research paper <http://dl.acm.org/citation.cfm?id=3098145>`__.
        
        |Watch promotional video|
        
        Download and installation
        =========================
        
        ``pathpy`` is pure python code. It has no platform-specific dependencies
        and should thus work on all platforms. It builds on ``numpy`` and
        ``scipy``. The latest version of ``pathpy`` can be installed by typing:
        
        ``> pip install git+git://github.com/IngoScholtes/pathpy.git``
        
        Tutorial
        ========
        
        A `comprehensive educational
        tutorial <https://ingoscholtes.github.io/pathpy/tutorial.html>`__ which
        shows how you can use ``pathpy`` to analyze data on pathways and
        temporal networks is `available
        online <https://ingoscholtes.github.io/pathpy/tutorial.html>`__.
        Moreover, a tutorial which illustrates the abstraction of **higher-order
        networks** in the modeling of dynamical processes in temporal networks
        is `available
        here <https://www.sg.ethz.ch/team/people/ischoltes/research-insights/temporal-networks-demo/>`__.
        The latter tutorial is based on the predecessor library
        ```pyTempNets`` <https://github.com/IngoScholtes/pyTempNets>`__. Most of
        its features have been ported to ``pathpy``.
        
        Documentation
        =============
        
        The code is fully documented via docstrings which are accessible through
        python's built-in help system. Just type ``help(SYMBOL_NAME)`` to see
        the documentation of a class or method. A `reference manual is available
        here <https://ingoscholtes.github.io/pathpy/hierarchy.html>`__.
        
        Releases and Versioning
        =======================
        
        The first public beta release of pathpy (released February 17 2017) is
        `v1.0-beta <https://github.com/IngoScholtes/pathpy/releases/tag/v1.0-beta.1>`__.
        Following versions are named MAJOR.MINOR.PATCH according to `semantic
        versioning <http://semver.org/>`__. The date of each release is encoded
        in the PATCH version.
        
        Acknowledgements
        ================
        
        The research behind this data analysis framework was funded by the Swiss
        State Secretariat for Education, Research and Innovation `(Grant
        C14.0036) <https://www.sg.ethz.ch/projects/seri-information-spaces/>`__.
        The development of this package was generously supported by the `MTEC
        Foundation <http://www.mtec.ethz.ch/research/support/MTECFoundation.html>`__
        in the context of the project `The Influence of Interaction Patterns on
        Success in Socio-Technical Systems: From Theory to
        Practice <https://www.sg.ethz.ch/projects/mtec-interaction-patterns/>`__.
        
        Contributors
        ============
        
        | `Ingo Scholtes <http://www.ingoscholtes.net>`__ (project lead,
          development)
        | Luca Verginer (development, test suite integration)
        | Roman Cattaneo (development)
        | Nicolas Wider (testing)
        
        Copyright
        =========
        
        ``pathpy`` is licensed under the `GNU Affero General Public
        License <https://choosealicense.com/licenses/agpl-3.0/>`__.
        
        (c) Copyright ETH Zürich, Chair of Systems Design, 2015-2017
        
        .. |Watch promotional video| image:: https://img.youtube.com/vi/CxJkVrD2ZlM/0.jpg
           :target: https://www.youtube.com/watch?v=CxJkVrD2ZlM
        
        
        =======
        History
        =======
        
        2.0.0a (2018-08-07)
        ------------------
        
        * First public release on PyPI.
        
        1.2.1 (2018-02-23)
        ------------------
        
        * First test release on PyPI.
        
Keywords: network analysis temporal networks pathways sequence modeling graph mining
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5
