@inproceedings{5805ccc5450542e9acfe9359cf56c252,
title = "Analysis and Visualisation of Time Series Data on Networks with Pathpy",
abstract = "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'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.",
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 Petrovic, {Luka V.} and Vincenzo Perri and Luca Verginer and Christoph Gote",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 30th World Wide Web Conference, WWW 2021 ; Conference date: 19-04-2021 Through 23-04-2021",
year = "2021",
month = apr,
day = "19",
doi = "10.1145/3442442.3452052",
language = "English (US)",
series = "The Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021",
publisher = "Association for Computing Machinery, Inc",
pages = "530--532",
booktitle = "The Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021",
}