Algorithmic Aspects of Temporal Betweenness

Sebastian Buß, Hendrik Molter, Rolf Niedermeier, MacIej Rymar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

The betweenness centrality of a graph vertex measures how often this vertex is visited on shortest paths between other vertices of the graph. In the analysis of many real-world graphs or networks, betweenness centrality of a vertex is used as an indicator for its relative importance in the network. In recent years, a growing number of real-world networks is modeled as temporal graphs instead of conventional (static) graphs. In a temporal graph, we have a fixed set of vertices and there is a finite discrete set of time steps and every edge might be present only at some time steps. While shortest paths are straightforward to define in static graphs, temporal paths can be considered "optimal" with respect to many different criteria, including length, arrival time, and overall travel time (shortest, foremost, and fastest paths). This leads to different concepts of temporal betweenness centrality, posing new challenges on the algorithmic side. We provide a systematic study of temporal betweenness variants based on various concepts of optimal temporal paths both on a theoretical and empirical level.

Original languageEnglish
Title of host publicationKDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2084-2092
Number of pages9
ISBN (Electronic)9781450379984
DOIs
StatePublished - 23 Aug 2020
Externally publishedYes
Event26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020 - Virtual, Online, United States
Duration: 23 Aug 202027 Aug 2020

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2020
Country/TerritoryUnited States
CityVirtual, Online
Period23/08/2027/08/20

Keywords

  • algorithm engineering
  • counting complexity
  • network centrality
  • network science
  • temporal graphs
  • temporal paths

ASJC Scopus subject areas

  • Software
  • Information Systems

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