pyTempNet is a python module for the analysis of time-stamped relational data represented as temporal networks. It particularly facilitates the analysis of temporal networks from the perspective of higher-order networks, which include causality limitations due to order correlations. This perspective and the resulting analytic measures have been outlined in the papers:

1. 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, Nature Communications, 5, Sept. 2014
2. R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer: Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks, Phys Rev Lett, 110(19), 198701, May 2013

The module is written in pure python, has no platform-specific dependencies and should thus work on all platforms. The latest development version can be installed by typing:

> pip install git+git://github.com/IngoScholtes/pyTempNets.git

A detailed educational tutorial showcasing the features of pyTempNet and illustrating its theoretical foundation is available online.

The development of this module was generously supported by the MTEC Foundation in the context of the project The Influence of Interaction Patterns on Success in Socio-Technical Systems: From Theory to Practice.

Ingo Scholtes (project lead, development)
Roman Cattaneo (development)

(c) Copyright ETH Zürich, Chair of Systems Design, 2015