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Betweenness in time dependent networks

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  • Alsayed, Ahmad
  • Higham, Desmond J.

Abstract

The concept of betweenness has given rise to a very useful class of network centrality measures. Loosely, betweenness quantifies the level of importance of a node in terms of its propensity to act as an intermediary when messages are passed around the network. In this work we generalize a walk-based betweenness measure to the case of time-dependent networks, such as those arising in telecommunications and on-line social media. We also introduce a new kind of betweenness measure, temporal betweenness, which quantifies the importance of a time-point. We illustrate the effectiveness of these new measures on synthetic examples, and also give results on real data sets involving voice call, email and Twitter.

Suggested Citation

  • Alsayed, Ahmad & Higham, Desmond J., 2015. "Betweenness in time dependent networks," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 35-48.
    • Handle: RePEc:eee:chsofr:v:72:y:2015:i:c:p:35-48
    DOI: 10.1016/j.chaos.2014.12.009
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    References listed on IDEAS

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    1. Estrada, Ernesto & Higham, Desmond J. & Hatano, Naomichi, 2009. "Communicability betweenness in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(5), pages 764-774.
    2. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    Cited by:

    1. Seabrook, Isobel & Barucca, Paolo & Caccioli, Fabio, 2022. "Structural importance and evolution: an application to financial transaction networks," LSE Research Online Documents on Economics 117130, London School of Economics and Political Science, LSE Library.
    2. Al Mugahwi, Mohammed & De La Cruz Cabrera, Omar & Fenu, Caterina & Reichel, Lothar & Rodriguez, Giuseppe, 2021. "Block matrix models for dynamic networks," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    3. Seabrook, Isobel & Barucca, Paolo & Caccioli, Fabio, 2022. "Structural importance and evolution: An application to financial transaction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Liu, Yang & Wei, Bo & Du, Yuxian & Xiao, Fuyuan & Deng, Yong, 2016. "Identifying influential spreaders by weight degree centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 1-7.

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