research-article

Efficient Exact and Approximate Betweenness Centrality Computation for Temporal Graphs

Authors:

Tianming Zhang,

Jing FanAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 2395 - 2406

https://doi.org/10.1145/3589334.3645438

Published: 13 May 2024 Publication History

Abstract

Betweenness centrality of a vertex in a graph evaluates how often the vertex occurs in the shortest paths. It is a widely used metric of vertex importance in graph analytics. While betweenness centrality on static graphs has been extensively investigated, many real-world graphs are time-varying and modeled as temporal graphs. Examples include social networks and telecommunication networks, where a relationship between two vertices occurs at a specific time. Hence, in this paper, we target efficient methods for temporal betweenness centrality computation. We firstly propose an exact algorithm with the new notion of time instance graph, based on which, we derive a temporal dependency accumulation theory for iterative computation. To reduce the size of the time instance graph and improve the efficiency, we propose an additional optimization, which compresses the time instance graph with equivalent vertices and edges, and extends the dependency theory to the compressed graph. Since it is theoretically complex to compute temporal betweenness centrality, we further devise a probabilistically guaranteed approximate method to handle massive temporal graphs. Extensive experimental results on real-world temporal networks demonstrate the superior performance of the proposed methods. In particular, our exact and approximate methods outperform the state-of-the-art methods by up to two and five orders of magnitude, respectively.

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Cited By

Sadhu SKumari KNamtirtha AMalta MDutta A(2025)TGNN-Bet: Approximation of Temporal Betweenness Centrality using Temporal Graph Neural Network2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885618(911-915)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885618
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
Wang XWang YLin XYu JGao HCheng XYu D(2024)Efficient Betweenness Centrality Computation over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368200617:11(3360-3372)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682006
Show More Cited By

Index Terms

Efficient Exact and Approximate Betweenness Centrality Computation for Temporal Graphs
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Graph algorithms analysis

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
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Program Chairs:
Ravi Kumar
Google
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Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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National Natural Science Foundation of China
Key Research Project of Zhejiang Province
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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

Singapore, Singapore

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Cited By

Sadhu SKumari KNamtirtha AMalta MDutta A(2025)TGNN-Bet: Approximation of Temporal Betweenness Centrality using Temporal Graph Neural Network2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885618(911-915)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885618
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
Wang XWang YLin XYu JGao HCheng XYu D(2024)Efficient Betweenness Centrality Computation over Large Heterogeneous Information NetworksProceedings of the VLDB Endowment10.14778/3681954.368200617:11(3360-3372)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.14778/3681954.3682006
Wang JWang KLin XZhang WZhang Y(2024)Efficient Unsupervised Community Search with Pre-Trained Graph TransformerProceedings of the VLDB Endowment10.14778/3665844.366585317:9(2227-2240)Online publication date: 6-Aug-2024
https://dl.acm.org/doi/10.14778/3665844.3665853
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
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

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