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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | No |
| Title | Analysis and Visualisation of Time Series Data on Networks with Pathpy |
| Organization Unit | |
| Authors |
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| Presentation Type | speech |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 530–532 |
| Event Title | Companion Proceedings of the Web Conference 2021 |
| Event Type | workshop |
| Event Location | Ljubljana Slovenia |
| Event Start Date | January 1 - 2021 |
| Event End Date | January 1 - 2021 |
| Place of Publication | New York, NY, USA |
| Publisher | Association for Computing Machinery |
| Abstract Text | 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. |
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