Analysis and Visualisation of Time Series Data on Networks with Pathpy
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Author / Producer
Date
2021-04
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Book title
WWW '21: Companion Proceedings of the Web Conference 2021
Journal / series
Volume
Pages / Article No.
530 - 532
Publisher
Association for Computing Machinery
Event
The Web Conference 2021 (WWW 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
higher-order graph models; temporal network; graph mining; network analysis; network visualization; causal paths; software
Organisational unit
03682 - Schweitzer, Frank (emeritus) / Schweitzer, Frank (emeritus)