Introduction
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:
- 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
- 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
Tutorial
A detailed educational tutorial showcasing the features of pyTempNet and illustrating its theoretical foundation is online here: https://www.sg.ethz.ch/team/people/ischoltes/research-insights/temporal-networks-demo/
Acknowledgements
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".
Contributors
Ingo Scholtes (project lead, development) Roman Cattaneo (development)
Copyright
- Copyright ETH Zürich, Chair of Systems Design, 2015