IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v592y2022ics0378437121009985.html
   My bibliography  Save this article

Source estimation in continuous-time diffusion networks via incomplete observation

Author

Listed:
  • Shi, Chaoyi
  • Zhang, Qi
  • Chu, Tianguang

Abstract

This paper considers the problem of estimating the source of diffusion in a network under incomplete observation condition. The diffusion process is described by a continuous-time information diffusion model and the source estimation is formulated as a maximum likelihood (ML) estimator in terms of a Gaussian weighted averaging of the correlation coefficients between the observed activation times and the sampled transmission delays obtained by Monte Carlo simulations. Experiments are worked out with both synthetic and real-world networks to show the effectiveness of our method in comparison with previous results.

Suggested Citation

  • Shi, Chaoyi & Zhang, Qi & Chu, Tianguang, 2022. "Source estimation in continuous-time diffusion networks via incomplete observation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    • Handle: RePEc:eee:phsmap:v:592:y:2022:i:c:s0378437121009985
    DOI: 10.1016/j.physa.2021.126843
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121009985
    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.2021.126843?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. Gajewski, Ł.G. & Suchecki, K. & Hołyst, J.A., 2019. "Multiple propagation paths enhance locating the source of diffusion in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 34-41.
    2. Xu, Shuaishuai & Teng, Cong & Zhou, Yinzuo & Peng, Junhao & Zhang, Yicheng & Zhang, Zi-Ke, 2019. "Identifying the diffusion source in complex networks with limited observers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    3. Huang, Qiangjuan & Zhao, Chengli & Zhang, Xue & Yi, Dongyun, 2017. "Locating the source of spreading in temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 434-444.
    4. Menin, Olavo H. & Bauch, Chris T., 2018. "Solving the patient zero inverse problem by using generalized simulated annealing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1513-1521.
    5. Chen, Lijuan & Hattaf, Khalid & Sun, Jitao, 2015. "Optimal control of a delayed SLBS computer virus model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 244-250.
    6. Devavrat Shah & Tauhid Zaman, 2016. "Finding Rumor Sources on Random Trees," Operations Research, INFORMS, vol. 64(3), pages 736-755, June.
    7. 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)

    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. Paluch, Robert & Gajewski, Łukasz G. & Suchecki, Krzysztof & Hołyst, Janusz A., 2021. "Impact of interactions between layers on source localization in multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    2. Anzhi Sheng & Qi Su & Aming Li & Long Wang & Joshua B. Plotkin, 2023. "Constructing temporal networks with bursty activity patterns," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. He, Yifan & Zhao, Chen & Zeng, An, 2022. "Ranking locations in a city via the collective home-work relations in human mobility data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    4. 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.
    5. Wang, Cheng-Jun & Wu, Lingfei, 2016. "The scaling of attention networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 196-204.
    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. Jing Yang & Yingwu Chen, 2011. "Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-5, July.
    8. Baltakys, Kęstutis & Kanniainen, Juho & Saramäki, Jari & Kivelä, Mikko, 2023. "Investor trade allocation patterns in stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 210(C), pages 191-209.
    9. Diao, Su-Meng & Liu, Yun & Zeng, Qing-An & Luo, Gui-Xun & Xiong, Fei, 2014. "A novel opinion dynamics model based on expanded observation ranges and individuals’ social influences in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 220-228.
    10. 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.
    11. 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.
    12. 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.
    13. Suisheng Yu & Mingcai Li & Fengming Liu, 2017. "Rumor Identification with Maximum Entropy in MicroNet," Complexity, Hindawi, vol. 2017, pages 1-8, September.
    14. Wu, Yingbo & Li, Pengdeng & Yang, Lu-Xing & Yang, Xiaofan & Tang, Yuan Yan, 2017. "A theoretical method for assessing disruptive computer viruses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 325-336.
    15. C Ben Gibson & Norbou Buchler & Blaine Hoffman & Claire-Genevieve La Fleur, 2019. "Participation shifts explain degree distributions in a human communications network," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-13, May.
    16. 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.
    17. Edward Anderson & David Gamarnik & Anton Kleywegt & Asuman Ozdaglar, 2016. "Preface to the Special Issue on Information and Decisions in Social and Economic Networks," Operations Research, INFORMS, vol. 64(3), pages 561-563, June.
    18. Huan-Kai Peng & Hao-Chih Lee & Jia-Yu Pan & Radu Marculescu, 2016. "Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-21, January.
    19. Sun, Ruoyan, 2016. "Optimal weight based on energy imbalance and utility maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 429-435.
    20. 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.

    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:592:y:2022:i:c:s0378437121009985. 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.