`pathpy`

is an Open Source python package providing higher-order network analytics for time series data.

`pathpy`

is tailored to analyse time-stamped network data as well as sequential data that capture multiple short paths observed in a graph or network. Examples for data that can be analysed with `pathpy`

include high-resolution time-stamped network data, dynamic social networks, user click streams on the Web, biological pathway data, citation graphs, passenger trajectories in transportation networks, or information propagation in social networks.

Unifying the analysis of time series data on networks, `pathpy`

provides efficient methods to extract causal or time-respecting paths in time-stamped social networks. It facilitates the analysis of higher-order dependencies and uses principled model selection techniques to infer models that capture both topological and temporal characteristics. It allows to answer the question when network models of time series data are justified and when higher-order models are needed.

`pathpy`

is fully integrated with `jupyter`

, providing rich interactive visualisations of networks, temporal networks, higher-, and multi-order models. Visualisations can be exported to HTML5 files that can be shared and published on the Web. You can find examples in our gallery.

The theoretical foundation of this package, higher- and multi-order network models, was developed in the following peer-reviewed research articles:

- R Lambiotte, M Rosvall, I Scholtes: From networks to optimal models of complex systems, Nature Physics 15, 313-320, March 2019
- 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
- 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
- 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
- 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

An explanatory video with a high-level introduction of the the science behind `pathpy`

is available here. A broader view on the importance of higher-order network models in network analysis can be found in this recent article.

A step-by-step introduction that shows how to install `pathpy`

and how to perform basic network analysis and visualisation tasks can be found here.

We further provide an extensive collection of educational resources, including lectures, tutorials, exercises, and data. If you are interested to host a such an educational event within your institution, please contact us.

`pathpy`

is released under the GNU Affero General Public License.