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IngoScholtes committed Feb 23, 2017
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<img src="https://github.com/IngoScholtes/pathpy/blob/master/pathpy_logo.png" width="300" />
<img src="https://github.com/IngoScholtes/pathpy/blob/master/pathpy_logo.png" width="300" alt="pathpy logo" />
# Introduction
@@ -23,16 +23,18 @@ The theoretical foundation of this package, **higher-order network models**, has
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
The module is written in pure python. It has no platform-specific dependencies and should thus work on all platforms. It builds on `numpy` and `scipy`.
<img src="https://github.com/IngoScholtes/pathpy/blob/master/multiorder.png" width="300" alt="Illustration of Multi-Order Model" />
The latest version of `pathpy` can be installed by typing:
# Download and installation
The module is written in pure python. 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 your data sets 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
A [comprehensive educational tutorial](https://ingoscholtes.github.io/pathpy/tutorial.html) which shows how you can use `pathpy` to analyze your data sets 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` but most of its features have already been included in `pathpy`.
# Documentation
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