pathpy Tutorial

In this tutorial, we will give an in-depth introduction to the Open Source python data analytics package pathpy. We illustrate the theoretical foundations of higher- and multi-order network models in toy examples, and we will demonstrate their advantages in real-world time series data on complex networks. The latest version of pathpy is publicly available via the python package index. You can simply install it by typing:

pip install pathpy2

pathpy is fully integrated with jupyter notebooks, providing in-line, interactive and dynamic visualisations of graphs and networks, temporal networks, as well as higher- and multi-order network models. This teaser video highlights some of its features:

Watch promotional video

The following video explains the science behind pathpy:

Watch promotional video

You can find the technical details in the following publications:

  1. I Scholtes: When is a network a network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks, 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, 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, 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, Phys Rev Lett, 110(19), 198701, May 2013

Having completed a brief introduction to interactive data science with python, Visual Studio Code, and jupyter in unit 1, this tutorial consists of seven units (20 - 30 minutes each). For each unit we provide a stand-alone HTML file, as well as a juypter notebook that you can download and run on your own machine. In unit 5 and 8 we invite you to use pathpy to explore higher- and multi-order models on your own.

Unit Topic notebook
2 Introducing pathpy ipynb
3 Higher-order analysis of path data ipynb
4 Temporal Network Analysis and Visualisation ipynb
5 Exploration: Higher-order analysis of time series data ipynb
6 Multi-order Representation Learning ipynb
7 Optimal higher-order analysis of temporal data ipnyb
8 Exploration: Representation learning in time series data ipynb

pathpy is brought to you by the Data Analytics Group at the IfI of University of Zurich. Feel free to contact us if you want to host an interaction hands-on tutorial session in your group, institute, or company.