research-article

Algorithmic Aspects of Temporal Betweenness

Authors:

Sebastian Buß,

Hendrik Molter,

Rolf Niedermeier,

Maciej RymarAuthors Info & Claims

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2084 - 2092

https://doi.org/10.1145/3394486.3403259

Published: 20 August 2020 Publication History

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.

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Cited By

Mertzios GMolter HRenken MSpirakis PZschoche P(2025)The Complexity of Transitively Orienting Temporal GraphsJournal of Computer and System Sciences10.1016/j.jcss.2025.103630(103630)Online publication date: Feb-2025
https://doi.org/10.1016/j.jcss.2025.103630
Zhang TZhao JYu CChen LGao YCao BFan JYu G(2025)Labeling-based centrality approaches for identifying critical edges on temporal graphsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3424-y19:2Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s11704-023-3424-y
Yan HYang ZZhang TWen DLuo QUkey N(2025)Approximating Temporal Katz Centrality with Monte Carlo MethodsWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_1(3-16)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_1
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Index Terms

Algorithmic Aspects of Temporal Betweenness
1. Mathematics of computing
  1. Discrete mathematics
    1. Combinatorics
      1. Combinatorial algorithms
    2. Graph theory
      1. Graph algorithms
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis

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cover image ACM Conferences

KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

August 2020

3664 pages

ISBN:9781450379984

DOI:10.1145/3394486

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UC San Diego, USA
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LG Electronics, USA
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Linkedin, USA
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Publications Chairs:
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Michigan State, USA
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Georgia Tech, USA

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Published: 20 August 2020

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Cited By

Mertzios GMolter HRenken MSpirakis PZschoche P(2025)The Complexity of Transitively Orienting Temporal GraphsJournal of Computer and System Sciences10.1016/j.jcss.2025.103630(103630)Online publication date: Feb-2025
https://doi.org/10.1016/j.jcss.2025.103630
Zhang TZhao JYu CChen LGao YCao BFan JYu G(2025)Labeling-based centrality approaches for identifying critical edges on temporal graphsFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3424-y19:2Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s11704-023-3424-y
Yan HYang ZZhang TWen DLuo QUkey N(2025)Approximating Temporal Katz Centrality with Monte Carlo MethodsWeb and Big Data. APWeb-WAIM 2024 International Workshops10.1007/978-981-96-0055-7_1(3-16)Online publication date: 31-Jan-2025
https://doi.org/10.1007/978-981-96-0055-7_1
Brunelli FCrescenzi PViennot LBaeza-Yates RBonchi F(2024)Making Temporal Betweenness Computation Faster and RestlessProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671825(163-174)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671825
Gionis AOettershagen LSarpe IChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Mining Temporal NetworksCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641245(1260-1263)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3641245
Zhang TFang JYang ZCao BFan JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)TATKC: A Temporal Graph Neural Network for Fast Approximate Temporal Katz Centrality RankingProceedings of the ACM Web Conference 202410.1145/3589334.3645432(527-538)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645432
Klobas NMertzios GMolter HSpirakis P(2024)The complexity of computing optimum labelings for temporal connectivityJournal of Computer and System Sciences10.1016/j.jcss.2024.103564(103564)Online publication date: Jul-2024
https://doi.org/10.1016/j.jcss.2024.103564
Cruciani A(2024)MANTRA: Temporal Betweenness Centrality Approximation Through SamplingMachine Learning and Knowledge Discovery in Databases. Research Track10.1007/978-3-031-70341-6_8(125-143)Online publication date: 22-Aug-2024
https://doi.org/10.1007/978-3-031-70341-6_8
Oettershagen LKriege NMutzel PSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Higher-Order Temporal H-Index for Evolving NetworksProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599242(1770-1782)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599242
Fluschnik TNiedermeier RSchubert CZschoche P(2023)Multistage s–t Path: Confronting Similarity with DissimilarityAlgorithmica10.1007/s00453-022-01077-w85:7(2028-2064)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s00453-022-01077-w
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