Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Temporal Walk Centrality: Ranking Nodes in Evolving Networks

Abstract

We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random walks that satisfy the time constraints of the network. We show that temporal walk centrality can identify nodes playing central roles in dissemination processes that might not be detected by related betweenness concepts and other common static and temporal centrality measures. We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. A technical contribution is a general approach to lift existing algebraic methods for counting walks in static networks to temporal networks. Our experiments on real-world temporal networks show the efficiency and accuracy of our algorithms. Finally, we demonstrate that the rankings by temporal walk centrality often differ significantly from those of other state-of-the-art temporal centralities.

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Authors
  • Oettershagen, Lutz
  • Mutzel, Petra
  • Kriege, Nils M.
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
The ACM Web Conference
Divisions
Data Mining and Machine Learning
Event Location
Lyon, France - virtual
Event Type
Conference
Event Dates
25.04.-29.04.2022
Series Name
WWW '22: Proceedings of the ACM Web Conference 2022
ISSN/ISBN
978-1-4503-9096-5
Page Range
pp. 1640-1650
Date
25 April 2022
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