IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp288-296.html
   My bibliography  Save this article

Navigating temporal networks

Author

Listed:
  • Lee, Sang Hoon
  • Holme, Petter

Abstract

Navigation on graphs is the problem how an agent walking on the graph can get from a source to a target with limited information about the graph. The information and the way to exploit it can vary. In this paper, we study navigation on temporal networks—networks where we have explicit information about the time of the interaction, not only who interacts with whom. We contrast a type of greedy navigation – where agents follow paths that would have worked well in the past – with two strategies that do not exploit the additional information. We test these on empirical temporal network data sets. The greedy navigation finds the targets faster and more reliably than the reference strategies, meaning that there are correlations in the real temporal networks that can be exploited. We find that both topological and temporal structures affect the navigation.

Suggested Citation

  • Lee, Sang Hoon & Holme, Petter, 2019. "Navigating temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 288-296.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:288-296
    DOI: 10.1016/j.physa.2018.09.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118311518
    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.2018.09.036?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. Karimi, Fariba & Holme, Petter, 2013. "Threshold model of cascades in empirical temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3476-3483.
    2. Jari Saramäki & Petter Holme, 2015. "Exploring temporal networks with greedy walks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(12), pages 1-8, December.
    3. Jon M. Kleinberg, 2000. "Navigation in a small world," Nature, Nature, vol. 406(6798), pages 845-845, August.
    4. 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.
    5. 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.
    6. Angélica Sousa da Mata & Romualdo Pastor-Satorras, 2015. "Slow relaxation dynamics and aging in random walks on activity driven temporal networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(2), pages 1-8, February.
    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. Xiaole Wan & Zhen Zhang & Chi Zhang & Qingchun Meng, 2020. "Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index," Complexity, Hindawi, vol. 2020, pages 1-19, July.

    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. 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.
    2. 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.
    3. 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.
    4. Fariba Karimi & Matthias Raddant, 2016. "Cascades in Real Interbank Markets," Computational Economics, Springer;Society for Computational Economics, vol. 47(1), pages 49-66, January.
    5. 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.
    6. Tian, Yang & Tian, Hui & Cui, Yajuan & Zhu, Xuzhen & Cui, Qimei, 2023. "Influence of behavioral adoption preference based on heterogeneous population on multiple weighted networks," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    7. Àlex Arenas & Antonio Cabrales & Leon Danon & Albert Díaz-Guilera & Roger Guimerà & Fernando Vega-Redondo, 2010. "Optimal information transmission in organizations: search and congestion," Review of Economic Design, Springer;Society for Economic Design, vol. 14(1), pages 75-93, March.
    8. Panayotis Christidis & Álvaro Gomez Losada, 2019. "Email Based Institutional Network Analysis: Applications and Risks," Social Sciences, MDPI, vol. 8(11), pages 1-14, November.
    9. Boris Salazar & María del Pilar Castillo, 2008. "Pobreza Urbana Y Exclusión Social De Los Desplazados," Documentos de Trabajo 4500, Universidad del Valle, CIDSE.
    10. Andrea Avena-Koenigsberger & Xiaoran Yan & Artemy Kolchinsky & Martijn P van den Heuvel & Patric Hagmann & Olaf Sporns, 2019. "A spectrum of routing strategies for brain networks," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-24, March.
    11. Blagus, Neli & Šubelj, Lovro & Bajec, Marko, 2012. "Self-similar scaling of density in complex real-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2794-2802.
    12. Douglas R. White & Jason Owen-Smith & James Moody & Walter W. Powell, 2004. "Networks, Fields and Organizations: Micro-Dynamics, Scale and Cohesive Embeddings," Computational and Mathematical Organization Theory, Springer, vol. 10(1), pages 95-117, May.
    13. 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).
    14. Luca Gallo & Lucas Lacasa & Vito Latora & Federico Battiston, 2024. "Higher-order correlations reveal complex memory in temporal hypergraphs," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    15. Cowan, Robin & Jonard, Nicolas & Sanditov, Bulat, 2009. "Fits and Misfits: Technological Matching and R&D Networks," MERIT Working Papers 2009-042, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    16. Amos Korman & Efrat Greenwald & Ofer Feinerman, 2014. "Confidence Sharing: An Economic Strategy for Efficient Information Flows in Animal Groups," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-10, October.
    17. Shi, Xiaolin & Adamic, Lada A. & Strauss, Martin J., 2007. "Networks of strong ties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(1), pages 33-47.
    18. Peter Biddle & Paul England & Marcus Peinado & Bryan Willman, 2003. "The Darknet and the Future of Content Distribution," Levine's Working Paper Archive 618897000000000636, David K. Levine.
    19. Joost Berkhout & Bernd F. Heidergott, 2019. "Analysis of Markov Influence Graphs," Operations Research, INFORMS, vol. 67(3), pages 892-904, May.
    20. 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.

    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:513:y:2019:i:c:p:288-296. 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.