IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v470y2017icp12-19.html
   My bibliography  Save this article

Empirical study of the role of the topology in spreading on communication networks

Author

Listed:
  • Medvedev, Alexey
  • Kertesz, Janos

Abstract

Topological aspects, like community structure, and temporal activity patterns, like burstiness, have been shown to severely influence the speed of spreading in temporal networks. We study the influence of the topology on the susceptible–infected (SI) spreading on time stamped communication networks, as obtained from a dataset of mobile phone records. We consider city level networks with intra- and inter-city connections. The networks using only intra-city links are usually sparse, where the spreading depends mainly on the average degree. The inter-city links serve as bridges in spreading, speeding up considerably the process. We demonstrate the effect also on model simulations.

Suggested Citation

  • Medvedev, Alexey & Kertesz, Janos, 2017. "Empirical study of the role of the topology in spreading on communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 12-19.
    • Handle: RePEc:eee:phsmap:v:470:y:2017:i:c:p:12-19
    DOI: 10.1016/j.physa.2016.11.109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116309323
    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.2016.11.109?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. Jean-Charles Delvenne & Renaud Lambiotte & Luis E. C. Rocha, 2015. "Diffusion on networked systems is a question of time or structure," Nature Communications, Nature, vol. 6(1), pages 1-10, November.
    2. Wu, Xiaoyan & Liu, Zonghua, 2008. "How community structure influences epidemic spread in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 623-630.
    3. Cristopher Moore & M. E. J. Newman, 2000. "Epidemics and Percolation in Small-World Networks," Working Papers 00-01-002, Santa Fe Institute.
    4. Albert-László Barabási, 2005. "The origin of bursts and heavy tails in human dynamics," Nature, Nature, vol. 435(7039), pages 207-211, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huang, Wei & Zhang, Tianyi & Yao, Xinwei, 2022. "Optimization for sequential communication line attack in interdependent power-communication network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    2. Xu, Xiao-Ting & Wang, Nianxin & Bian, Jun & Zhou, Bin, 2019. "Understanding the diversity on power-law-like degree distribution in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 576-581.
    3. Tu, Haicheng & Xia, Yongxiang & Wu, Jiajing & Zhou, Xiang, 2019. "Robustness assessment of cyber–physical systems with weak interdependency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 9-17.
    4. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    5. Zhou, Bin & Xu, Xiao-Ting & Liu, Jian-Guo & Xu, Xiao-Ke & Wang, Nianxin, 2019. "Information interaction model for the mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1170-1176.

    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. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Qiu, Tian & Shi, Yong-Dong & Zhong, Chen-Yang, 2015. "Coupled effects of local movement and global interaction on contagion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 482-491.
    2. Shang, Jiaxing & Liu, Lianchen & Li, Xin & Xie, Feng & Wu, Cheng, 2015. "Epidemic spreading on complex networks with overlapping and non-overlapping community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 171-182.
    3. Lu, Xi & Mo, Hongming & Deng, Yong, 2015. "An evidential opinion dynamics model based on heterogeneous social influential power," Chaos, Solitons & Fractals, Elsevier, vol. 73(C), pages 98-107.
    4. Ganjeh-Ghazvini, Mostafa & Masihi, Mohsen & Ghaedi, Mojtaba, 2014. "Random walk–percolation-based modeling of two-phase flow in porous media: Breakthrough time and net to gross ratio estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 214-221.
    5. Shang, Jiaxing & Liu, Lianchen & Li, Xin & Xie, Feng & Wu, Cheng, 2016. "Targeted revision: A learning-based approach for incremental community detection in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 70-85.
    6. Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
    7. Pan, Ya-Nan & Lou, Jing-Jing & Han, Xiao-Pu, 2014. "Outbreak patterns of the novel avian influenza (H7N9)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 265-270.
    8. Zhou, Bin & Xie, Jia-Rong & Yan, Xiao-Yong & Wang, Nianxin & Wang, Bing-Hong, 2017. "A model of task-deletion mechanism based on the priority queueing system of Barabási," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 415-421.
    9. Chen, Ning & Zhu, Xuzhen & Chen, Yanyan, 2019. "Information spreading on complex networks with general group distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 671-676.
    10. Zhenpeng Li & Xijin Tang & Zhenjie Hong, 2022. "Collective attention dynamic induced by novelty decay," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 95(8), pages 1-11, August.
    11. Greg Morrison & L Mahadevan, 2012. "Discovering Communities through Friendship," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    12. Kotnis, Bhushan & Kuri, Joy, 2016. "Cost effective campaigning in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 670-681.
    13. Floortje Alkemade & Carolina Castaldi, 2005. "Strategies for the Diffusion of Innovations on Social Networks," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 3-23, February.
    14. Koen Zwet & Ana I. Barros & Tom M. Engers & Peter M. A. Sloot, 2022. "Emergence of protests during the COVID-19 pandemic: quantitative models to explore the contributions of societal conditions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    15. Qin, Yang & Zhong, Xiaoxiong & Jiang, Hao & Ye, Yibin, 2015. "An environment aware epidemic spreading model and immune strategy in complex networks," Applied Mathematics and Computation, Elsevier, vol. 261(C), pages 206-215.
    16. Velarde, Carlos & Robledo, Alberto, 2021. "Statistical mechanical model for growth and spread of contagions under gauged population confinement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    17. 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.
    18. Qianqian Liu & Qun Wang, 2017. "A comparative study on uncooperative search models in survivor search and rescue," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 843-857, November.
    19. Kota Yamada & Atsunori Kanemura, 2020. "Simulating bout-and-pause patterns with reinforcement learning," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-21, November.
    20. Muaz Niazi & Amir Hussain, 2011. "Agent-based computing from multi-agent systems to agent-based models: a visual survey," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(2), pages 479-499, November.

    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:470:y:2017:i:c:p:12-19. 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.