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HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs

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Graph Drawing and Network Visualization (GD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12590))

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Abstract

Network visualisation techniques are important tools for the exploratory analysis of complex systems. While these methods are regularly applied to visualise data on complex networks, we increasingly have access to time series data that can be modelled as temporal networks or dynamic graphs. In dynamic graphs, the temporal ordering of time-stamped edges determines the causal topology of a system, i.e., which nodes can, directly and indirectly, influence each other via a so-called causal path. This causal topology is crucial to understand dynamical processes, assess the role of nodes, or detect clusters. However, we lack graph drawing techniques that incorporate this information into static visualisations. Addressing this gap, we present a novel dynamic graph visualisation algorithm that utilises higher-order graphical models of causal paths in time series data to compute time-aware static graph visualisations. These visualisations combine the simplicity and interpretability of static graphs with a time-aware layout algorithm that highlights patterns in the causal topology that result from the temporal dynamics of edges .

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Acknowledgements

Vincenzo Perri and Ingo Scholtes acknowledge support by the Swiss National Science Foundation, grant 176938. Ingo Scholtes acknowledges support by the project bergisch.smart.mobility, funded by the German state of North Rhine-Westphalia.

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Authors and Affiliations

  1. Data Analytics Group, Department of Informatics (IfI), University of Zurich, Zürich, Switzerland

    Vincenzo Perri & Ingo Scholtes

  2. Chair of Data Analytics, Faculty of Mathematics and Natural Sciences, University of Wuppertal, Wuppertal, Germany

    Ingo Scholtes

Authors
  1. Vincenzo Perri
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  2. Ingo Scholtes
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Correspondence to Vincenzo Perri .

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Editors and Affiliations

  1. LaBRI, University of Bordeaux, Talence, France

    David Auber

  2. Charles University, Prague, Czech Republic

    Pavel Valtr

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Perri, V., Scholtes, I. (2020). HOTVis: Higher-Order Time-Aware Visualisation of Dynamic Graphs. In: Auber, D., Valtr, P. (eds) Graph Drawing and Network Visualization. GD 2020. Lecture Notes in Computer Science(), vol 12590. Springer, Cham. https://doi.org/10.1007/978-3-030-68766-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-68766-3_8

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  • Online ISBN: 978-3-030-68766-3

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