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Toward a generalized notion of discrete time for modeling temporal networks

Published online by Cambridge University Press:  25 January 2022

Konstantin Kueffner  and
Mark Strembeck Open the ORCID record for Mark Strembeck [Opens in a new window]
Konstantin Kueffner
Affiliation:
Vienna University of Economics and Business, WU, Vienna, Austria Secure Business Austria Research Center (SBA), Vienna, Austria
Mark Strembeck*
Affiliation:
Vienna University of Economics and Business, WU, Vienna, Austria Secure Business Austria Research Center (SBA), Vienna, Austria Complexity Science Hub Vienna (CSH), Vienna, Austria
*
*Corresponding author. Email: [email protected]
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Abstract

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Many real-world networks, including social networks and computer networks for example, are temporal networks. This means that the vertices and edges change over time. However, most approaches for modeling and analyzing temporal networks do not explicitly discuss the underlying notion of time. In this paper, we therefore introduce a generalized notion of discrete time for modeling temporal networks. Our approach also allows for considering nondeterministic time and incomplete data, two issues that are often found when analyzing datasets extracted from online social networks, for example. In order to demonstrate the consequences of our generalized notion of time, we also discuss the implications for the computation of (shortest) temporal paths in temporal networks. In addition, we implemented an R-package that provides programming support for all concepts discussed in this paper. The R-package is publicly available for download.

Keywords

multilayer networkmultivalued pathtemporal networktemporal pathtimeweighted network
Type
Research Article
Information
Network Science , Volume 9 , Issue 4 , December 2021 , pp. 443 - 477
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

Action Editor: Ulrik Brandes

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