@inproceedings{866,
  keywords = {causal paths, graph mining, higher-order graph models, network analysis, network visualization, software, temporal network},
  author = {J{\"u}rgen Hackl and Ingo Scholtes and Luka Petrovi{\'c} and Vincenzo Perri and Luca Verginer and Christoph Gote},
  title = {Analysis and visualisation of time series data on networks with pathpy},
  abstract = {<p>The Open Source software package pathpy, available at https://www.pathpy.net, implements statistical techniques to learn optimal graphical models for the causal topology generated by paths in time-series data. Operationalizing Occam{\textquoteright}s razor, these models balance model complexity with explanatory power for empirically observed paths in relational time series. Standard network analysis is justified if the inferred optimal model is a first-order network model. Optimal models with orders larger than one indicate higher-order dependencies and can be used to improve the analysis of dynamical processes, node centralities and clusters.</p>
},
  year = {2021},
  journal = {ACM Web Conference},
  pages = {530{\textendash}532},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  isbn = {978-1-4503-8313-4},
  url = {https://doi.org/10.1145/3442442.3452052},
  doi = {10.1145/3442442.3452052},
  note = {Number of pages: 3 Place: Ljubljana, Slovenia},
}