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Memory in network flows and its effects on spreading dynamics and community detection

Author

Listed:
  • Martin Rosvall

    (Integrated Science Lab, Umeå University)

  • Alcides V. Esquivel

    (Integrated Science Lab, Umeå University)

  • Andrea Lancichinetti

    (Integrated Science Lab, Umeå University
    Howard Hughes Medical Institute (HHMI), Northwestern University)

  • Jevin D. West

    (Integrated Science Lab, Umeå University
    Information School, University of Washington)

  • Renaud Lambiotte

    (University of Namur)

Abstract

Random walks on networks is the standard tool for modelling spreading processes in social and biological systems. This first-order Markov approach is used in conventional community detection, ranking and spreading analysis, although it ignores a potentially important feature of the dynamics: where flow moves to may depend on where it comes from. Here we analyse pathways from different systems, and although we only observe marginal consequences for disease spreading, we show that ignoring the effects of second-order Markov dynamics has important consequences for community detection, ranking and information spreading. For example, capturing dynamics with a second-order Markov model allows us to reveal actual travel patterns in air traffic and to uncover multidisciplinary journals in scientific communication. These findings were achieved only by using more available data and making no additional assumptions, and therefore suggest that accounting for higher-order memory in network flows can help us better understand how real systems are organized and function.

Suggested Citation

  • Martin Rosvall & Alcides V. Esquivel & Andrea Lancichinetti & Jevin D. West & Renaud Lambiotte, 2014. "Memory in network flows and its effects on spreading dynamics and community detection," Nature Communications, Nature, vol. 5(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5630
    DOI: 10.1038/ncomms5630
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    Cited by:

    1. Vitanov, Nikolay K. & Vitanov, Kaloyan N., 2018. "Discrete-time model for a motion of substance in a channel of a network with application to channels of human migration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 635-650.
    2. Chao Min & Qingyu Chen & Erjia Yan & Yi Bu & Jianjun Sun, 2021. "Citation cascade and the evolution of topic relevance," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 110-127, January.
    3. Mandana Saebi & Jian Xu & Erin K Grey & David M Lodge & James J Corbett & Nitesh Chawla, 2020. "Higher-order patterns of aquatic species spread through the global shipping network," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-24, July.
    4. Feng, Liang & Zhao, Qianchuan & Zhou, Cangqi, 2020. "Epidemic in networked population with recurrent mobility pattern," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    5. 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).
    6. Chakraborty, Abhijit & Krichene, Hazem & Inoue, Hiroyasu & Fujiwara, Yoshi, 2019. "Characterization of the community structure in a large-scale production network in Japan," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 210-221.
    7. Yang, Jin-Xuan, 2020. "The spreading of infectious diseases with recurrent mobility of community population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    8. 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.
    9. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2022. "Temporal networks in the analysis of financial contagion," Working Paper Series 2667, European Central Bank.
    10. 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.
    11. Livi, Lorenzo & Maiorino, Enrico & Pinna, Andrea & Sadeghian, Alireza & Rizzi, Antonello & Giuliani, Alessandro, 2016. "Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 199-214.
    12. Tatsuro Kawamoto & Ryutaro Hashimoto, 2021. "Identifying macroscopic features in foreign visitor travel pathways," The Japanese Economic Review, Springer, vol. 72(1), pages 129-144, January.
    13. Vitanov, Nikolay K. & Vitanov, Kaloyan N., 2018. "On the motion of substance in a channel of a network and human migration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1277-1294.
    14. Kovalenko, K. & Romance, M. & Vasilyeva, E. & Aleja, D. & Criado, R. & Musatov, D. & Raigorodskii, A.M. & Flores, J. & Samoylenko, I. & Alfaro-Bittner, K. & Perc, M. & Boccaletti, S., 2022. "Vector centrality in hypergraphs," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    15. Li, Jin-Yue & Li, Xiang & Li, Cong, 2021. "The Kronecker-clique model for higher-order clustering coefficients," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    16. 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).
    17. Wang, Yufang & Wang, Haiyan & Zhang, Shuhua, 2018. "A weighted higher-order network analysis of fine particulate matter (PM2.5) transport in Yangtze River Delta," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 654-662.
    18. Wu, Qingchu & Zhou, Rong & Hadzibeganovic, Tarik, 2019. "Conditional quenched mean-field approach for recurrent-state epidemic dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 71-79.
    19. Vitanov, Nikolay K. & Borisov, Roumen & Vitanov, Kaloyan N., 2021. "On the motion of substance in a channel and growth of random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    20. Zhong, Li-Xin & Xu, Wen-Juan & Chen, Rong-Da & Zhong, Chen-Yang & Qiu, Tian & Shi, Yong-Dong & Wang, Li-Liang, 2016. "A generalized voter model with time-decaying memory on a multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 95-105.

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