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Ranking in interconnected multilayer networks reveals versatile nodes

Author

Listed:
  • Manlio De Domenico

    (Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili)

  • Albert Solé-Ribalta

    (Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili)

  • Elisa Omodei

    (Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili
    LaTTiCe-CNRS, École Normale Supérieure
    Institut des Systèmes Complexes - Paris Île-de-France (ISC-PIF))

  • Sergio Gómez

    (Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili)

  • Alex Arenas

    (Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili
    IPHES, Institut Català de Paleoecologia Humana i Evolució Social)

Abstract

The determination of the most central agents in complex networks is important because they are responsible for a faster propagation of information, epidemics, failures and congestion, among others. A challenging problem is to identify them in networked systems characterized by different types of interactions, forming interconnected multilayer networks. Here we describe a mathematical framework that allows us to calculate centrality in such networks and rank nodes accordingly, finding the ones that play the most central roles in the cohesion of the whole structure, bridging together different types of relations. These nodes are the most versatile in the multilayer network. We investigate empirical interconnected multilayer networks and show that the approaches based on aggregating—or neglecting—the multilayer structure lead to a wrong identification of the most versatile nodes, overestimating the importance of more marginal agents and demonstrating the power of versatility in predicting their role in diffusive and congestion processes.

Suggested Citation

  • Manlio De Domenico & Albert Solé-Ribalta & Elisa Omodei & Sergio Gómez & Alex Arenas, 2015. "Ranking in interconnected multilayer networks reveals versatile nodes," Nature Communications, Nature, vol. 6(1), pages 1-6, November.
    • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7868
    DOI: 10.1038/ncomms7868
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    10. Jin, Haiyan & Zhang, ChenXing & Ma, Mengzhou & Gong, Qianhua & Yu, Liang & Guo, Xingli & Gao, Lin & Wang, Bingbo, 2020. "Inferring essential proteins from centrality in interconnected multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
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    12. Quan Ye & Guanghui Yan & Wenwen Chang & Hao Luo, 2023. "Vital node identification based on cycle structure in a multiplex network," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 96(2), pages 1-16, February.
    13. Beheshtian-Ardakani, Arash & Salehi, Mostafa & Sharma, Rajesh, 2023. "CMPN: Modeling and analysis of soccer teams using Complex Multiplex Passing Network," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
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    15. Annamaria Ficara & Giacomo Fiumara & Salvatore Catanese & Pasquale De Meo & Xiaoyang Liu, 2022. "The Whole Is Greater than the Sum of the Parts: A Multilayer Approach on Criminal Networks," Future Internet, MDPI, vol. 14(5), pages 1-21, April.
    16. Higham, Kyle & Contisciani, Martina & De Bacco, Caterina, 2022. "Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    17. Stella, Massimo, 2020. "Multiplex networks quantify robustness of the mental lexicon to catastrophic concept failures, aphasic degradation and ageing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    18. Aleja, David & Criado, Regino & García del Amo, Alejandro J. & Pérez, Ángel & Romance, Miguel, 2019. "Non-backtracking PageRank: From the classic model to hashimoto matrices," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 283-291.
    19. 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).
    20. Óskarsdóttir, María & Bravo, Cristián, 2021. "Multilayer network analysis for improved credit risk prediction," Omega, Elsevier, vol. 105(C).
    21. Zhang, Ting & Zhang, Kun & Lv, Laishui & Bardou, Dalal, 2019. "Co-Ranking for nodes, layers and timestamps in multilayer temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 88-96.
    22. Wang, Longjian & Zheng, Shaoya & Wang, Yonggang & Wang, Longfei, 2021. "Identification of critical nodes in multimodal transportation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    23. Gong, Xiao-Li & Liu, Jian-Min & Xiong, Xiong & Zhang, Wei, 2022. "Research on stock volatility risk and investor sentiment contagion from the perspective of multi-layer dynamic network," International Review of Financial Analysis, Elsevier, vol. 84(C).
    24. Tripathi, Richa & Reza, Amit, 2020. "A subset selection based approach to structural reducibility of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    25. Wang, Longjian & Zhang, Shuichao & Szűcs, Gábor & Wang, Yonggang, 2024. "Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method," Reliability Engineering and System Safety, Elsevier, vol. 244(C).

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