A python module for the analysis of time-stamped relational data represented as temporal networks.
Latest commit 751bbaf Feb 23, 2017 @IngoScholtes committed on GitHub Update README.md
added reference to new package pathpy



pyTempNet is a python module for the analysis of time-stamped relational data. It particularly facilitates the analysis of temporal networks from the perspective of higher-order networks, a powerful framework which has been introduced in the following recent articles and which allows to overcome some of the limitations of common network-analytic methods:

  1. I Scholtes, N Wider, A Garas: Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path Structures and centralities, EPJ B, 89:61, March 2 2016, arXiv 1508.06467
  2. 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
  3. 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

Note: The development of pyTempNets continues in the new python module pathpy. This new package unifies the analysis of temporal networks and pathways, and provides principled model selection techniques to determine the optimal order of higher-order network representations. Switching to pathpy is thus strongly suggested.

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

While the current master branch only allows to generate second-order networks, the development branch can be used to analyze higher-order networks with arbitrary order as introduced in our works. Please stay tuned for our forthcoming stable release that will include many more feartures.


A detailed educational ipython 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)
Nicolas Wider (testing, development)


(c) Copyright ETH Zürich, Chair of Systems Design, 2015-2016