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1

Traag, Vincent. Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06391-1.

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2

Stavros, Siokos, ed. Financial networks: Statics and dynamics. Berlin: Springer-Verlag, 1997.

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3

Menache, Ishai. Network games: Theory, models, and dynamics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2011.

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4

A, Arbib Michael, Amari Shun'ichi y U.S.-Japan Seminar on Competition and Cooperation in Neural Nets (1987 : University of Southern California), eds. Dynamic interactions in neural networks: Models and data. New York: Springer-Verlag, 1989.

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5

G, Chen. Fundamentals of complex networks: Models, structures, and dynamics. Singapore: John Wiley & Sons Inc., 2015.

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6

Arbib, Michael A. y Shun-ichi Amari, eds. Dynamic Interactions in Neural Networks: Models and Data. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4612-4536-0.

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7

Aldo, Romano y SpringerLink (Online service), eds. Dynamic Learning Networks: Models and Cases in Action. Boston, MA: Springer-Verlag US, 2009.

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8

M, Harris-Warrick Ronald, ed. Dynamic biological networks: The stomatogastric nervous system. Cambridge, Mass: MIT Press, 1992.

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9

Aura, Reggiani y Nijkamp Peter, eds. Spatial dynamics, networks and modelling. Cheltenham, UK: Edward Elgar, 2006.

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10

H, Gartner Nathan, Improta Gennaro 1942- y International Seminar on Urban Traffic Networks (2nd : 1992 : Capri, Italy), eds. Urban traffic networks: Dynamic flow modeling and control. Berlin: Springer-Verlag, 1995.

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11

Watts, Duncan J. Small worlds: The dynamics of networks between order and randomness. Princeton, N.J: Princeton University Press, 1999.

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12

A legal framework for emerging business models: Dynamic networks as collaborative contracts. Cheltenham, Glos, UK: Edward Elgar, 2012.

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13

Billings, S. A. The Determination of multivariable nonlinear models for dynamic systems using neural networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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14

1981-, Lin W. Sabrina y Liu, K. J. Ray, 1961-, eds. Behavior dynamics in media-sharing social networks. Cambridge: Cambridge University Press, 2011.

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15

Ran, Bin. Modeling dynamic transportation networks: An intelligent transportation system oriented approach. 2a ed. Berlin: Springer, 1996.

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16

Jer-Nan, Juang, Hyland David C y Langley Research Center, eds. On neural networks in identification and control of dynamic systems. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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17

Introduction to dynamic systems modeling for design. Englewood Cliffs, N.J: Prentice Hall, 1994.

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18

Workshop on Dynamic Social Network Modeling and Analysis (2002 Washington, D.C.). Dynamic Social Network Modeling and Analysis: Workshop summary and papers. Washington, D.C: National Academies Press, 2003.

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19

Lee, Jihui. Varying-Coefficient Models and Functional Data Analyses for Dynamic Networks and Wearable Device Data. [New York, N.Y.?]: [publisher not identified], 2018.

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20

Leenders, Roger Th A. J. Structure and influence: Statistical models for the dynamics of actor attributes, network structure, and their interdependence. Amsterdam: Thesis Publishers, 1995.

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21

1952-, Reinhardt J., ed. Neural networks: An introduction. Berlin: Springer-Verlag, 1990.

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22

Müller, Berndt. Neural networks: An introduction. 2a ed. Berlin: Springer-Verlag, 1991.

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23

Müller, Berndt. Neural networks: An introduction. 2a ed. Berlin: Springer, 1995.

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24

Deventer, Rainer. Modeling and control of static and dynamic systems with Bayesian networks. Berlin: Logos, 2004.

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25

Chen, Luonan. Modeling biomolecular networks in cells: Structures and dynamics. London: Springer, 2010.

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26

Ran, Bin. Modeling dynamic transportation networks: An intelligent transportation system oriented approach : with 51 figures. 2a ed. Berlin: Springer-Verlag, 1996.

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27

Mitra, Partha. Observed brain dynamics. New York: Oxford University Press, 2008.

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28

The dynamics of regions and networks in industrial ecosystems. Cheltenham: Edward Elgar, 2009.

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29

Liu, Jinkun. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems: Design, Analysis and Matlab Simulation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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30

Rasmussen, T. C. Fluid flow and solute transport modeling through three-dimensional networks of variably saturated discrete fractures. Washington, DC: Division of Engineering, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1989.

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31

Horst, Bunke, ed. A graph-theoretic approach to enterprise network dynamics. Boston: Birkhäuser, 2007.

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32

Wuyi, Yue, Takahashi Yataka y Takagi Hideaki, eds. Advances in queueing theory and network applications. New York, N.Y: Springer, 2009.

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33

1964-, Beim Graben P., ed. Lectures in supercomputational neuroscience: Dynamics in complex brain networks. Berlin: Springer, 2008.

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34

Guzman, Mark G. Accounting for fluctuations in social network usage and migration dynamics. [Dallas, Tx.]: Federal Reserve Bank of Dallas, 2004.

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35

Cogan, Susan M. Catholic Social Networks in Early Modern England. NL Amsterdam: Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463726948.

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Catholic Social Networks in Early Modern England: Kinship, Gender, and Coexistence explores the lived experience of Catholic women and men in the post-Reformation century. Set against the background of the gendered dynamics of English society, this book demonstrates that English Catholics were potent forces in the shaping of English culture, religious policy, and the emerging nation-state. Drawing on kinship and social relationships rooted in the medieval period, post Reformation English Catholic women and men used kinship, social networks, gendered strategies, political actions, and cultural activities like architecture and gardening to remain connected to patrons and to ensure the survival of their families through a period of deep social and religious change. This book contributes to recent scholarship on religious persecution and coexistence in post-Reformation Europe by demonstrating how English Catholics shaped state policy and enforcement of religious minorities and helped to define the character of early models of citizenship formation.
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36

Ran, Bin y David E. Boyce. Dynamic Urban Transportation Network Models. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-662-00773-0.

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37

Salinelli, Ernesto y Franco Tomarelli. Discrete Dynamical Models. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02291-8.

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38

1968-, Jacqmin-Gadda Hélène, ed. Dynamical biostatistical models. Boca Raton: CRC Press/Taylor & Francis, 2016.

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39

Borsboom, Denny. Mental disorders, network models, and dynamical systems. Editado por Kenneth S. Kendler y Josef Parnas. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198796022.003.0011.

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Network approaches have been proposed as an alternative way of thinking about relations between symptoms of mental disorders. Unlike traditional psychometric approaches, network models view these associations as the result of direct interactions between symptoms. Disorders are defined as alternative stable states of a network due to increased connectivity between symptoms. This increased connectivity creates a pattern of reinforcement, so the system can get stuck in a state of prolonged activation. Mental health is defined as the stable state of a weakly connected network. Although symptomatology may be temporarily increased in a healthy network (e.g., due to adverse life events), as the influence of a shock wanes the network will spontaneously return to its healthy state. Strongly connected networks, however, may transition into disordered states upon similar external shocks, and may not naturally recover. Thus, the proposed definitions yield plausible conceptualizations of resilience and vulnerability.
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40

Kendler, Kenneth S. Introduction to “Mental disorders, network models, and dynamical systems”. Editado por Kenneth S. Kendler y Josef Parnas. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198796022.003.0010.

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This chapter presents an introduction to mental disorders, network models, and dynamical systems. It outlines the approach taken in the following chapter, and discusses five main points: development of networks from empirical data; interconnected sets of symptoms; network models and “stress to the system”; “levels of connectivity”; and hysteresis of networks.
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41

Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Springer, 2014.

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42

Traag, Vincent. Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Springer International Publishing AG, 2016.

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43

Traag, Vincent. Algorithms and Dynamical Models for Communities and Reputation in Social Networks. Springer London, Limited, 2014.

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44

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 the relevant of network properties against null models are here described. Moreover, Multi-slice temporal networks Models capturing main properties of real temporal network data are presented. Finally, null models of general multilayer networks and networks of networks are characterized.
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45

Dynamic Interactions in Neural Networks: Models and Data. Springer, 2011.

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46

Li, Xiang, Xiaofan Wang y Guanrong Chen. Fundamentals of Complex Networks: Models, Structures and Dynamics. Wiley & Sons, Incorporated, John, 2014.

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47

Li, Xiang, Xiaofan Wang y Guanrong Chen. Fundamentals of Complex Networks: Models, Structures and Dynamics. Wiley & Sons, Limited, John, 2015.

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48

Li, Xiang, Xiaofan Wang y Guanrong Chen. Fundamentals of Complex Networks: Models, Structures and Dynamics. Wiley & Sons, Incorporated, John, 2014.

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49

Amari, Shun-Ichi y Michael A. Arbib. Dynamic Interactions in Neural Networks: Models and Data. Springer London, Limited, 2012.

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50

Secundo, Giustina y Aldo Romano. Dynamic Learning Networks: Models and Cases in Action. Springer, 2010.

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