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What is pathpyG?

pathpyG is an Open Source package facilitating GPU-accelerated next-generation network analytics and graph learning for time series data on graphs.

pathpyG is tailored to analyse time-stamped network data as well as sequential data that capture multiple short walks or paths observed in a graph or network. Examples for data that can be analysed with pathpyG include high-resolution time-stamped network data, dynamic social networks, user click streams on the Web, biological pathway data, directed acyclic graphs like citation networks, passenger trajectories in transportation networks, or trajectories of information propagation in social networks.

pathpyG is fully integrated with jupyter, providing rich interactive visualisations of networks, temporal networks, and higher-order models. Visualisations can be exported to HTML5 files that can be shared and published on the Web.

What is the science behind pathpyG?

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

  1. L Qarkaxhija, V Perri, I Scholtes: De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs, In Proceedings of the First Learning on Graphs Conference, PMLR 198:51:1-51:21, December 2022
  2. L Petrovic, I Scholtes: Learning the Markov order of paths in graphs, In Proceedings of WWW '22: The Web Conference 2022, Lyon, France, April 2022
  3. V Perri, I Scholtes: HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs, In Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020), Vancouver, BC, Canada, September 15-18, 2020
  4. 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
  5. 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
  6. 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
  7. 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

A broader view on the importance of higher-order graph models for complex systems can be found in this overview article.