IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v593y2022ics0378437122000644.html
   My bibliography  Save this article

A method to compute the communicability of nodes through causal paths in temporal networks

Author

Listed:
  • Funel, Agostino

Abstract

We present a method aimed to compute the communicability (broadcast and receive) of nodes through causal paths in temporal networks. The method considers all possible combinations of chronologically ordered products of adjacency matrices of the network snapshots and by means of a damping procedure favors the paths that have high communication efficiency. We apply the method to four real-world networks of face-to-face human contacts and identify the nodes with high communicability. The accuracy of the method is proved by studying the spread of an epidemic in the networks using the susceptible–infected–recovered model. We show that if a node with high broadcast is chosen as the origin of the outbreak of infection then the epidemic spreads early while it is delayed and inhibited if the origin of infection is a node with low broadcast. Receiving nodes can be treated as broadcasters if the arrow of time is reversed.

Suggested Citation

  • Funel, Agostino, 2022. "A method to compute the communicability of nodes through causal paths in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    • Handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000644
    DOI: 10.1016/j.physa.2022.126965
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122000644
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.126965?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julie Fournet & Alain Barrat, 2014. "Contact Patterns among High School Students," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-17, September.
    2. Ingo Scholtes & Nicolas Wider & Antonios Garas, 2016. "Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(3), pages 1-15, March.
    3. Wang, Dan & Huang, Wei-Qiang, 2021. "Centrality-based measures of financial institutions’ systemic importance: A tail dependence network view," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    4. Ingo Scholtes & Nicolas Wider & René Pfitzner & Antonios Garas & Claudio J. Tessone & Frank Schweitzer, 2014. "Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    5. Yang, Xu-Hua & Xiong, Zhen & Ma, Fangnan & Chen, Xiaoze & Ruan, Zhongyuan & Jiang, Peng & Xu, Xinli, 2021. "Identifying influential spreaders in complex networks based on network embedding and node local centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    6. Ciro Cattuto & Wouter Van den Broeck & Alain Barrat & Vittoria Colizza & Jean-François Pinton & Alessandro Vespignani, 2010. "Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-9, July.
    7. Martin Rosvall & Alcides V. Esquivel & Andrea Lancichinetti & Jevin D. West & Renaud Lambiotte, 2014. "Memory in network flows and its effects on spreading dynamics and community detection," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
    8. Ingo Scholtes & Nicolas Wider & Antonios Garas, 2016. "Higher-order aggregate networks in the analysis of temporal networks: path structures and centralities," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(3), pages 1-15, March.
    9. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew Mellor, 2019. "Event Graphs: Advances And Applications Of Second-Order Time-Unfolded Temporal Network Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-26, May.
    2. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2024. "Temporal networks and financial contagion," Journal of Financial Stability, Elsevier, vol. 71(C).
    3. Mandana Saebi & Jian Xu & Erin K Grey & David M Lodge & James J Corbett & Nitesh Chawla, 2020. "Higher-order patterns of aquatic species spread through the global shipping network," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.
    4. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    5. Xie, Fengjie & Ma, Mengdi & Ren, Cuiping, 2022. "Research on multilayer network structure characteristics from a higher-order model: The case of a Chinese high-speed railway system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    6. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    7. Cañizares, Jessica R. & Reed, J. Michael & Fefferman, Nina H., 2024. "Network theory and migration: Avoiding misapplications and misinterpretations," Ecological Modelling, Elsevier, vol. 496(C).
    8. Cuiping Ren & Bianbian Chen & Fengjie Xie & Xuan Zhao & Jiaqian Zhang & Xueyan Zhou, 2022. "Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    9. Bi, Jialin & Jin, Ji & Qu, Cunquan & Zhan, Xiuxiu & Wang, Guanghui & Yan, Guiying, 2021. "Temporal gravity model for important node identification in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    10. Ayana Aspembitova & Ling Feng & Valentin Melnikov & Lock Yue Chew, 2019. "Fitness preferential attachment as a driving mechanism in bitcoin transaction network," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    11. Yan Zhang & Frank Schweitzer, 2021. "Quantifying the importance of firms by means of reputation and network control," Papers 2101.05010, arXiv.org.
    12. Xiang Li & Chengli Zhao & Zhaolong Hu & Caixia Yu & Xiaojun Duan, 2022. "Revealing the character of journals in higher-order citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6315-6338, November.
    13. Valeria Secchini & Javier Garcia-Bernardo & Petr Jansk'y, 2025. "Avoiding Overfitting in Variable-Order Markov Models: a Cross-Validation Approach," Papers 2501.14476, arXiv.org.
    14. Carolina Mattsson, 2019. "Networks of monetary flow at native resolution," Papers 1910.05596, arXiv.org.
    15. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.
    16. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    17. Gregory, Steve, 2012. "Ordered community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2752-2763.
    18. De Martino, Giuseppe & Spina, Serena, 2015. "Exploiting the time-dynamics of news diffusion on the Internet through a generalized Susceptible–Infected model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 634-644.
    19. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    20. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:593:y:2022:i:c:s0378437122000644. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.