Abstract
The concept of aggregate-network of multilayer network (MLN), which in many cases significantly simplifies the study of intersystem interactions is introduced, and the properties of its k-cores are investigated. The notion of p-cores is determined, with help of which the components of MLN that are directly involved in the implementation of intersystem interactions are distinguished. Methods of reducing the complexity of multilayer network models are investigated, which allow us to significantly decrease their dimensionality and better understand the processes that take place in intersystem interactions of different types. Effective scenarios of simultaneous group and system-wide targeted attacks on partially overlapped multilayer networks have been proposed, the main attention of which is focused on the transition points of MLN through which the intersystem interactions are actually implemented. It is shown that these scenarios can also be used to solve the inverse problem, namely, which elements of MLN should be blocked in the first place to prevent the acceleration of spread of dangerous infectious epidemics diseases, etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Scott, W.R., Davis, G.F.: Organizations and organizing: rational, natural and open systems perspectives. Routledge, London (2015)
Boccaletti S et al (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122. https://doi.org/10.1016/j.physrep.2014.07.001
Berlingerio, M., Coscia, M., Giannotti, F., Monreale, A., Pedreschi, D.: Multidimensional networks: foundations of structural analysis. World Wide Web 16(5–6), 567–593 (2012). https://doi.org/10.1007/s11280-012-0190-4
Polishchuk, O., Yadzhak, M.: Network structures and systems: II. Cores of networks and multiplexes. Sys. Res. Inf. Tech. 3, 38–51 (2018). https://doi.org/10.20535/SRIT.2308-8893.2018.3.04
Barabasi, A.-L.: The architecture of complexity. IEEE Cont. Sys. Mag. 27(4), 33–42 (2007). https://doi.org/10.1109/MCS.2007.384127
Northrop, R.B.: Introduction to complexity and complex systems. CRC Press, Boca Raton (2011)
Polishchuk O (2021) Aggregate-networks and p-cores of monoflow partially overlapped multilayer systems. arXiv:2111.10764v1
Lee, K.M., Min, B., Goh, K.I.: Towards real-world complexity: an introduction to multiplex networks. Eur. Phys. J. B 88, 48 (2015). https://doi.org/10.1140/epjb/e2015-50742-1
Corominas-Murtra, B., Fuchs, B., Thurner, S.: Detection of the elite structure in a virtual multiplex social system by means of a generalised K-core. PLoS ONE 9(12), e112606 (2014). https://doi.org/10.1371/journal.pone.0112606
Alvarez-Zuzek, L.G., Di Muro, M.A., Havlin, S., Braunstein, L.A.: Dynamic vaccination in partially overlapped multiplex network. Phys. Rev. E 99, 012302 (2019). https://doi.org/10.1103/PhysRevE.99.012302
Ghariblou, S., Salehi, M., Magnani, M., Jalili, M.: Shortest paths in multiplex networks. Sci. Rep. 7, 2142 (2017). https://doi.org/10.1038/s41598-017-01655-x
Ventura da Silva PC et al (2018) Epidemic spreading with awareness and different time scales in multiplex networks. arXiv:1812.01386v1
Osat, S., Radicchi, F., Papadopoulos, F.: k-core structure of real multiplex networks. Phys. Rev. Res. 2(2), 023176 (2020). https://doi.org/10.1103/PhysRevResearch.2.023176
Albert, R., Barabasi, A.-L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002). https://doi.org/10.1103/RevModPhys.74.47
Albert, R., Barabási, A.-L.: Error and attack tolerance of complex networks. Nature 406, 378–482 (2000). https://doi.org/10.1038/35019019
Al, B., et al.: Graph structure in the web. Comput. Netw. 33, 309–320 (2000). https://doi.org/10.1515/9781400841356.183
Holme, P., Kim, B.J., Yoon, C.N., Han, S.K.: Attack vulnerability of complex networks. Phys. Rev. E 65, 056109 (2002). https://doi.org/10.1103/PhysRevE.65.056109
Faria do Valle Í (2020) Recent advances in network medicine: from disease mechanisms to new treatment strategies. Multiple Sclerosis J 26(5):609–615. https://doi.org/10.1177/1352458519877002
https://24tv.ua/kiberataka-ukrayina-2022-novini-pro-napad-na-uryadovi-sayti_n1842293
Polishchuk, O.D.: Vulnerability of complex network structures and systems. Cybern. Syst. Anal. 56(2), 312–321 (2020). https://doi.org/10.1007/s10559-020-00247-4
Nasiri, E., Berahmand, K., Li, Y.: A new link prediction in multiplex networks using topologically biased random walks. Chaos Solitons Fractals 151, 111230 (2021). https://doi.org/10.1016/j.chaos.2021.111230
Nasiri E et al (2022) Impact of centrality measures on the common neighbors in link prediction for multiplex networks. Big Data 10(2):138–150. https://doi.org/10.1089/big.2021.0254
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Polishchuk, O. (2023). Structural Cores and Problems of Vulnerability of Partially Overlapped Multilayer Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Miccichè, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1077. Springer, Cham. https://doi.org/10.1007/978-3-031-21127-0_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-21127-0_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21126-3
Online ISBN: 978-3-031-21127-0
eBook Packages: EngineeringEngineering (R0)