I am the lead developer of pathpy, an OpenSource python package for the analysis of time series data on networks using higher-order and multi-order graphical models.
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.