IDEAS home Printed from https://ideas.repec.org/a/spr/aqjoor/v21y2023i2d10.1007_s10288-022-00507-3.html
   My bibliography  Save this article

A study of connectivity on dynamic graphs: computing persistent connected components

Author

Listed:
  • Mathilde Vernet

    (Avignon Université)

  • Yoann Pigné

    (Normandie Univ)

  • Éric Sanlaville

    (Normandie Univ)

Abstract

This work focuses on connectivity in a dynamic graph. An undirected graph is defined on a finite and discrete time interval. Edges can appear and disappear over time. The first objective of this work is to extend the notion of connected component to dynamic graphs in a new way. Persistent connected components are defined by their size, corresponding to the number of vertices, and their length, corresponding to the number of consecutive time steps they are present on. The second objective of this work is to develop an algorithm computing the largest, in terms of size and length, persistent connected components in a dynamic graph. PICCNIC algorithm (PersIstent Connected CompoNent InCremental Algorithm) is a polynomial time algorithm of minimal complexity. Another advantage of this algorithm is that it works online: knowing the evolution of the dynamic graph is not necessary to execute it. PICCNIC algorithm is implemented using the GraphStream library and experimented in order to carefully study the outcome of the algorithm according to different input graph types, as well as real data networks, to verify the theoretical complexity, and to confirm its feasibility for graphs of large size.

Suggested Citation

  • Mathilde Vernet & Yoann Pigné & Éric Sanlaville, 2023. "A study of connectivity on dynamic graphs: computing persistent connected components," 4OR, Springer, vol. 21(2), pages 205-233, June.
  • Handle: RePEc:spr:aqjoor:v:21:y:2023:i:2:d:10.1007_s10288-022-00507-3
    DOI: 10.1007/s10288-022-00507-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10288-022-00507-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10288-022-00507-3?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. Démare, Thibaut & Bertelle, Cyrille & Dutot, Antoine & Lévêque, Laurent, 2017. "Modeling logistic systems with an agent-based model and dynamic graphs," Journal of Transport Geography, Elsevier, vol. 62(C), pages 51-65.
    2. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
    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. 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.
    2. Panayotis Christidis & Álvaro Gomez Losada, 2019. "Email Based Institutional Network Analysis: Applications and Risks," Social Sciences, MDPI, vol. 8(11), pages 1-14, November.
    3. Viljoen, Nadia M. & Joubert, Johan W., 2019. "Supply chain micro-communities in urban areas," Journal of Transport Geography, Elsevier, vol. 74(C), pages 211-222.
    4. Dantsuji, Takao & Sugishita, Kashin & Fukuda, Daisuke, 2023. "Understanding changes in travel patterns during the COVID-19 outbreak in the three major metropolitan areas of Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    5. Hongyong Wang & Ping Xu & Fengwei Zhong, 2022. "Modeling and Feature Analysis of Air Traffic Complexity Propagation," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    6. Pietro DeLellis & Anna DiMeglio & Franco Garofalo & Francesco Lo Iudice, 2017. "The evolving cobweb of relations among partially rational investors," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    7. Karan, Rituraj & Biswal, Bibhu, 2017. "A model for evolution of overlapping community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 380-390.
    8. 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.
    9. Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    10. Chae, Bongsug (Kevin), 2019. "A General framework for studying the evolution of the digital innovation ecosystem: The case of big data," International Journal of Information Management, Elsevier, vol. 45(C), pages 83-94.
    11. 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.
    12. Christos Ellinas & Christos Nicolaides & Naoki Masuda, 2022. "Mitigation strategies against cascading failures within a project activity network," Journal of Computational Social Science, Springer, vol. 5(1), pages 383-400, May.
    13. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    14. Radu Tanase & Claudio J Tessone & René Algesheimer, 2018. "Identification of influencers through the wisdom of crowds," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    15. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    16. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    17. Daizaburo Shizuka & Allison E Johnson & Leigh Simmons, 2020. "How demographic processes shape animal social networks," Behavioral Ecology, International Society for Behavioral Ecology, vol. 31(1), pages 1-11.
    18. Fei Wang & Zhenfang Zhu & Peiyu Liu & Peipei Wang, 2019. "Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect," Future Internet, MDPI, vol. 11(4), pages 1-16, April.
    19. Li, Huichun & Zhang, Xue & Zhao, Chengli, 2021. "Explaining social events through community evolution on temporal networks," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    20. Nie, Chun-Xiao, 2022. "Generalized correlation dimension and heterogeneity of network spaces," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).

    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:spr:aqjoor:v:21:y:2023:i:2:d:10.1007_s10288-022-00507-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.