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Structural reducibility of multilayer networks

Author

Listed:
  • Manlio De Domenico

    (Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira I Virgili, Avda Paisos Catalans 26, Tarragona 43007, Spain)

  • Vincenzo Nicosia

    (School of Mathematical Sciences, Queen Mary University of London)

  • Alexandre Arenas

    (Departament d’Enginyeria Informática i Matemátiques, Universitat Rovira I Virgili, Avda Paisos Catalans 26, Tarragona 43007, Spain)

  • Vito Latora

    (School of Mathematical Sciences, Queen Mary University of London
    Università di Catania and INFN)

Abstract

Many complex systems can be represented as networks consisting of distinct types of interactions, which can be categorized as links belonging to different layers. For example, a good description of the full protein–protein interactome requires, for some organisms, up to seven distinct network layers, accounting for different genetic and physical interactions, each containing thousands of protein–protein relationships. A fundamental open question is then how many layers are indeed necessary to accurately represent the structure of a multilayered complex system. Here we introduce a method based on quantum theory to reduce the number of layers to a minimum while maximizing the distinguishability between the multilayer network and the corresponding aggregated graph. We validate our approach on synthetic benchmarks and we show that the number of informative layers in some real multilayer networks of protein–genetic interactions, social, economical and transportation systems can be reduced by up to 75%.

Suggested Citation

  • Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
  • Handle: RePEc:nat:natcom:v:6:y:2015:i:1:d:10.1038_ncomms7864
    DOI: 10.1038/ncomms7864
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    1. Robert Jankowski & Antoine Allard & Marián Boguñá & M. Ángeles Serrano, 2023. "The D-Mercator method for the multidimensional hyperbolic embedding of real networks," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
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    3. Charley Presigny & Marie-Constance Corsi & Fabrizio De Vico Fallani, 2024. "Node-layer duality in networked systems," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    4. Kosztyán, Zsolt T. & Csizmadia, Tibor & Katona, Attila I., 2021. "SIMILAR – Systematic iterative multilayer literature review method," Journal of Informetrics, Elsevier, vol. 15(1).
    5. Zhu, Xuzhen & Wang, Ruijie & Wang, Zexun & Chen, Xiaolong & Wang, Wei & Cai, Shimin, 2019. "Double-edged sword effect of edge overlap on asymmetrically interacting spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 617-624.
    6. Zhang, Wenjun & Deng, Weibing & Li, Wei, 2018. "Statistical properties of links of network: A survey on the shipping lines of Worldwide Marine Transport Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 218-227.
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    8. Luka Naglić & Lovro Šubelj, 2019. "War pact model of shrinking networks," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-14, October.
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    10. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    11. Laura Alessandretti & Luis Guillermo Natera Orozco & Meead Saberi & Michael Szell & Federico Battiston, 2023. "Multimodal urban mobility and multilayer transport networks," Environment and Planning B, , vol. 50(8), pages 2038-2070, October.
    12. Wang, Ning & Jin, Zi-Yang & Zhao, Jiao, 2021. "Cascading failures of overload behaviors on interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    13. Konda, Bruhan & González‐Sauri, Mario & Cowan, Robin & Yashodha, Yashodha & Chellattan Veettil, Prakashan, 2021. "Social networks and agricultural performance: A multiplex analysis of interactions among Indian rice farmers," MERIT Working Papers 2021-030, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. 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).
    15. Marialisa Scatá & Barbara Attanasio & Aurelio La Corte, 2021. "Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics," Future Internet, MDPI, vol. 13(5), pages 1-17, May.
    16. Qing Cai & Mahardhika Pratama & Sameer Alam, 2019. "Interdependency and Vulnerability of Multipartite Networks under Target Node Attacks," Complexity, Hindawi, vol. 2019, pages 1-16, November.
    17. Saha, Papri & Sarkar, Debasish, 2022. "Allometric scaling of von Neumann entropy in animal connectomes and its evolutionary aspect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    18. Jing Yang & Disheng Yi & Jingjing Liu & Yusi Liu & Jing Zhang, 2019. "Spatiotemporal Change Characteristics of Nodes’ Heterogeneity in the Directed and Weighted Spatial Interaction Networks: Case Study within the Sixth Ring Road of Beijing, China," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
    19. Li, Liqiang & Liu, Jing, 2020. "The aggregation of multiplex networks based on the similarity of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    20. 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).
    21. Klophaus, Richard & Lordan, Oriol, 2018. "Codesharing network vulnerability of global airline alliances," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 1-10.
    22. Natarajan Meghanathan, 2019. "Unit Disk Graph-Based Node Similarity Index for Complex Network Analysis," Complexity, Hindawi, vol. 2019, pages 1-22, March.
    23. Kk{e}stutis Baltakys & Juho Kanniainen & Frank Emmert-Streib, 2017. "Multilayer Aggregation with Statistical Validation: Application to Investor Networks," Papers 1708.09850, arXiv.org, revised May 2018.
    24. Riccardo Muolo & Joseph D. O’Brien & Timoteo Carletti & Malbor Asllani, 2024. "Persistence of chimera states and the challenge for synchronization in real-world networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 97(1), pages 1-16, January.
    25. 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).

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