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Books on the topic 'Multilayered network'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Stübing, Hagen. Multilayered Security and Privacy Protection in Car-to-X Networks: Solutions from Application down to Physical Layer. Springer Fachmedien Wiesbaden, 2013.

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2

Krawczak, Maciej. Multilayer Neural Networks. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00248-4.

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3

Masih, Lawrence. Associative recall in multilayered logical neural networks. Brunel University, 1990.

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4

De Domenico, Manlio. Multilayer Networks: Analysis and Visualization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-75718-2.

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5

McGee, Fintan, Mohammad Ghoniem, Benoît Otjacques, et al. Visual Analysis of Multilayer Networks. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-02608-9.

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6

Webb, Karen. Building Cisco multilayer switched networks. Cisco Press, 2000.

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7

C, Bass John, and Robinson James E. III, eds. Building Cisco multilayer switching networks. McGraw-Hill, 2000.

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8

Balaji, Sivasubramanian, and Frahim Erum, eds. Building Cisco multilayer switched networks (BCMSN). 4th ed. Cisco Press, 2007.

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9

Thomas, Thomas M. BCMSN: Building Cisco multilayer switching networks. McGraw-Hill, 2000.

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10

Luttrell, Stephen P. Self-supervision in multilayer adaptive networks. HMSO, 1991.

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11

Huang, Sihan. Community Detection in Social Networks: Multilayer Networks and Pairwise Covariates. [publisher not identified], 2020.

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12

Stübing, Hagen. Multilayered Security and Privacy Protection in Car-to-X Networks. Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-658-02531-1.

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13

Manfredi, Sabato. Multilayer Control of Networked Cyber-Physical Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41646-5.

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14

Balaji, Sivasubramanian, and Frahim Erum, eds. Building Cisco multilayer switched networks (BCMSN): CCNP self-study. 2nd ed. Cisco, 2004.

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15

Dmitrii, Lozovanu, and SpringerLink (Online service), eds. Optimization and Multiobjective Control of Time-Discrete Systems: Dynamic Networks and Multilayered Structures. Springer-Verlag Berlin Heidelberg, 2009.

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16

A, Kotfila David, ed. CCNP building multilayer switched networks (BCMSN 642-812) lab portfolio. Cisco Press, 2008.

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17

S, Spina Michelle, and United States. National Aeronautics and Space Administration., eds. Application of multilayer feedforward neural networks to precipitation cell-top altitude estimation. National Aeronautics and Space Administration, 1998.

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18

Program, Cisco Networking Academy, and Cisco Systems Inc, eds. Cisco Networking Academy Program: CCNP 3 : multilayer switching lab companion. 2nd ed. Cisco Press, 2005.

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19

Eugster, Johannes, Camilo Tovar Mora, Juan Trevino, Theofanis Papamichalis, and Nathan Porter. Systemic Impact of Debt Default in a Multilayered Global Network Model. International Monetary Fund, 2022.

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20

McVeety, Joseph J. Analysis of planar multi-conductor multilayered structures by network analog method. 1999.

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21

Eugster, Johannes, Camilo Tovar Mora, Juan Trevino, Theofanis Papamichalis, and Nathan Porter. Systemic Impact of Debt Default in a Multilayered Global Network Model. International Monetary Fund, 2022.

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22

McVeety, Joseph J. Analysis of planar multi-conductor multilayered structures by network analog method. 1999.

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23

Eugster, Johannes, Camilo Tovar Mora, Juan Trevino, Theofanis Papamichalis, and Nathan Porter. Systemic Impact of Debt Default in a Multilayered Global Network Model. International Monetary Fund, 2022.

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24

Stübing, Hagen. Multilayered Security and Privacy Protection in Car-to-X Networks: Solutions from Application down to Physical Layer. Springer Vieweg, 2013.

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25

Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.

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Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; sp

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26

Bianconi, Ginestra. Multilayer Network Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0010.

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This chapter presents the existing modelling frameworks for multiplex and multilayer networks. Multiplex network models are divided into growing multiplex network models and null models of multiplex networks. Growing multiplex networks are here shown to explain the main dynamical rules responsible to the emergent properties of multiplex networks, including the scale-free degree distribution, interlayer degree correlations and multilayer communities. Null models of multiplex networks are described in the context of maximum-entropy multiplex network ensembles. Randomization algorithms to test th

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27

Bianconi, Ginestra. Complex Systems as Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0001.

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Chapter 1 constitutes Part I of the book: ‘Single and Multilayer Networks’. This chapter introduces multilayer networks as an important new development of Network Science that allows a more comprehensive understanding of Complex Systems. It identifies the main motivations driving the research activity in this field of multilayer networks and emphasizes the benefits of taking a multilayer network perpective to characterize network data. The main advantages of a multilayer network approach with respect to the more traditional single layer characterization of complex networks are broadly discusse

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28

Bianconi, Ginestra. Interdependent Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0011.

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This chapter characterizes interdependent multilayer networks and their increased fragility. Interdependent networks are stylized models that can represent different complex systems, ranging from global infrastructures to molecular networks in the cell. When a fraction of nodes is initially damaged, interdependent networks are affected by dramatic cascades of failures that suddenly dismantle the multilayer network. The theory beyond this phenomenology is discussed in a pedagogical way by characterizing the percolation, discontinuous and hybrid transitions. The interplay between structure and f

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29

Rossi, Luca, Mark E. Dickison, and Matteo Magnani. Multilayer Social Networks. Cambridge University Press, 2016.

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30

Rossi, Luca, Mark E. Dickison, and Matteo Magnani. Multilayer Social Networks. Cambridge University Press, 2016.

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31

Rossi, Luca, Mark E. Dickison, and Matteo Magnani. Multilayer Social Networks. Cambridge University Press, 2016.

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32

Rossi, Luca, Mark E. Dickison, and Matteo Magnani. Multilayer Social Networks. Cambridge University Press, 2016.

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33

Bianconi, Ginestra. Multilayer Networks in Nature, Society and Infrastructures. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0004.

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Chapter 4 opens Part III of the book, ‘Multilayer Networks’, which comprises chapters 4–15. The chapter starts with an informal definition of multiplex networks, multi-slice networks and networks of networks, and motivates the research interest on multilayer networks by providing a general overview of the multiple applications of the multilayer network framework in different disciplines and contexts, including social networks, complex infrastructures, financial networks, molecular networks and network medicine, brain networks, ecological networks and climate networks. This chapter discusses th

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34

Bianconi, Ginestra. The Mathematical Definition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0005.

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This chapter provides the mathematical definition of multilayer networks and it is of fundamental importance for the rest of the book. The mathematical definition of multilayer networks is given in full generality and subsequently applied to specific types of multilayer networks including multiplex networks, multi-slice networks and networks of networks, motivating the discussion with examples and applications. The fundamental terminology used in multilayer networks is here introduced, including replica nodes, supernetworks and the supra-adjacency matrix. Additionally, this chapter describes t

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35

Bianconi, Ginestra. Basic Structural Properties. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0006.

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In this chapter the basic structural properties of multilayer networks are given. This chapter reveals that on multilayer networks the most basic structural properties of a network such as the degree or the clustering coefficient are also significantly modified. Therefore, it is necessary to define the multiplex degree and the multiplex degree distribution, the multilayer degree and the multilayer degree distribution, and the multilayer clustering coefficients. The chapter also discusses the relation between the properties of multiplex and multi-slice networks and the corresponding properties

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36

Bianconi, Ginestra. Epidemic Spreading. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0013.

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Epidemic processes are relevant to studying the propagation of infectious diseases, but their current use extends also to the study of propagation of ideas in the society or memes and news in online social media. In most of the relevant applications epidemic spreading does not actually take place on a single network but propagates in a multilayer network where different types of interaction play different roles. This chapter provides a comprehensive view of the effect that multilayer network structures have on epidemic processes. The Susceptible–Infected–Susceptible (SIS) Model and the Suscept

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37

Bianconi, Ginestra. Opinion Dynamics and Game Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0016.

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This chapter is devoted to opinion dynamics and game theory on multilayer networks. Since in social systems multilayer networks are the rule, it is particularly relevant to extend the modelling opinion dynamics to the multilayer network scenario. This chapter focuses in particular on the Voter Model, its variants, the Co-evolving Voter Model and models of competing networks, including election models showing that multiplexity has a major role in determining opinion dynamics. In particular, opinion dynamics on multilayer networks is not reducible to opinion dynamics on single layer networks. Fi

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38

Bianconi, Ginestra. Diffusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0014.

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This chapter addresses diffusion, random walks and congestion in multilayer networks. Here it is revealed that diffusion on a multilayer network can be significantly speed up with respect to diffusion taking place on its single layers taken in isolation, and that sometimes it is possible also to observe super-diffusion. Diffusion is here characterized on multilayer network structures by studying the spectral properties of the supra-Laplacian and the dependence on the diffusion constant among different layers. Random walks and its variations including the Lévy Walk are shown to reflect the impr

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39

Bianconi, Ginestra. Synchronization, Non-linear Dynamics and Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0015.

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This chapter is entirely devoted to characterizing non-linear dynamics on multilayer networks. Special attention is given to recent results on the stability of synchronization that extend the Master Stability Function approach to the multilayer networks scenario. Discontinous synchronization transitions on multiplex networks recently reported in the literature are also discussed, and their application discussed in the context of brain networks. This chapter also presents an overview of the major results regarding pattern formation in multilayer networks, and the proposed characterization of mu

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40

CCNP Building Multilayer Switched Networks. BPB Publications, 2004.

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41

Renoust, Benjamin, Mohammad Ghoniem, Andreas Kerren, Daniel Archambault, and Fintan McGee. Visual Analysis of Multilayer Networks. Morgan & Claypool Publishers, 2021.

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42

Renoust, Benjamin, Mohammad Ghoniem, Andreas Kerren, Daniel Archambault, and Fintan McGee. Visual Analysis of Multilayer Networks. Springer International Publishing AG, 2021.

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43

Liska. Building Dell Powerconnect Multilayer Networks. Pearson Education, Limited, 2004.

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44

Bianconi, Ginestra. Multilayer Networks: Structure and Function. Oxford University Press, 2018.

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45

Bianconi, Ginestra. Multilayer Networks: Structure and Function. Oxford University Press, 2022.

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46

McGee, Fintan. Visual Analysis of Multilayer Networks. Morgan & Claypool Publishers, 2021.

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47

McGee, Fintan. Visual Analysis of Multilayer Networks. Morgan & Claypool Publishers, 2021.

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48

Bianconi, Ginestra. The Structure of Single Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0002.

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Chapters 2–3 constitute Part II of the book, ‘Single Networks’, and provide a reference point for the rest of the book devoted exclusively to Multilayer Networks, making the book self-contained. This chapter provides the relevant background on the network structure of complex networks formed by just one layer (single networks). Here the basic definitions of network structure are given, the major network universalities are presented and methods to extract relevant information from network structure including centrality measures and community detection methods are discussed. Finally, modelling f

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49

Bianconi, Ginestra. Classical Percolation, Generalized Percolation and Cascades. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0012.

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This chapter characterizes the robustness of multiplex and multilayer networks using classical percolation, directed percolation and antagonistic percolation. Classical percolation determines whether a finite fraction of nodes of the multilayer networks are connected by any type of connection. Classical percolation can be affected by multiplexity since the degree correlations among different layers can modulate the robustness of the entire multilayer network. Directed percolation describes the propagation of a disease requiring cooperative infection from different layers of the multiplex netwo

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50

Building switched networks: Multilayer switching, QoS, IP multicast, network policy, and service level agreements. Addison-Wesley, 1999.

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