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Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks

Author

Listed:
  • Ingo Scholtes

    (ETH Zurich, Chair of Systems Design)

  • Nicolas Wider

    (ETH Zurich, Chair of Systems Design)

  • René Pfitzner

    (ETH Zurich, Chair of Systems Design)

  • Antonios Garas

    (ETH Zurich, Chair of Systems Design)

  • Claudio J. Tessone

    (ETH Zurich, Chair of Systems Design)

  • Frank Schweitzer

    (ETH Zurich, Chair of Systems Design)

Abstract

Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6024
    DOI: 10.1038/ncomms6024
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    Cited by:

    1. Ambra Amico & Luca Verginer & Giona Casiraghi & Giacomo Vaccario & Frank Schweitzer, 2023. "Adapting to Disruptions: Flexibility as a Pillar of Supply Chain Resilience," Papers 2304.05290, arXiv.org.
    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. 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).
    4. Rabbani, Fereshteh & Khraisha, Tamer & Abbasi, Fatemeh & Jafari, Gholam Reza, 2021. "Memory effects on link formation in temporal networks: A fractional calculus approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    5. 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.
    6. Marco Bardoscia & Fabio Caccioli & Juan Ignacio Perotti & Gianna Vivaldo & Guido Caldarelli, 2016. "Distress Propagation in Complex Networks: The Case of Non-Linear DebtRank," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.
    7. 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.
    8. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2022. "Temporal networks in the analysis of financial contagion," Working Paper Series 2667, European Central Bank.
    9. 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.
    10. 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.
    11. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    12. 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).
    13. 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).
    14. Martina Contisciani & Federico Battiston & Caterina De Bacco, 2022. "Inference of hyperedges and overlapping communities in hypergraphs," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    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.

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