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Books on the topic 'Complex dynamical network models'

Author: Grafiati

Published: 19 February 2023

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1

1965-, Barthélemy Marc, and Vespignani Alessandro 1965-, eds. Dynamical processes on complex networks. Cambridge: Cambridge University Press, 2009.

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2

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

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3

Shoikhet, David, and Mark Elin. Linearization Models for Complex Dynamical Systems. Basel: Birkhäuser Basel, 2010. http://dx.doi.org/10.1007/978-3-0346-0509-0.

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4

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

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5

Coherent dynamics of complex quantum systems. Berlin: Springer, 2004.

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6

Abarbanel, Henry. Predicting the Future: Completing Models of Observed Complex Systems. New York, NY: Springer New York, 2013.

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7

Kuipers, Benjamin. Self-calibrating models for dynamic monitoring and diagnosis: Final report covering the period 1 February 1992 to 31 March 1995. Austin, Tex: University of Texas at Austin, 1996.

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8

Mikhailov, A. S. From cells to societies: Models of complex coherent action. Berlin: Springer, 2002.

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9

Mukherjee, Animesh. Dynamics On and Of Complex Networks, Volume 2: Applications to Time-Varying Dynamical Systems. New York, NY: Springer New York, 2013.

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10

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

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11

service), SpringerLink (Online, ed. Evolutionary Games in Complex Topologies: Interplay Between Structure and Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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12

Michel, Olivier, Hiroki Sayama, and René Doursat. Morphogenetic engineering: Toward programmable complex systems. Heidelberg: Springer, 2013.

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13

Doursat, René. Morphogenetic Engineering: Toward Programmable Complex Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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14

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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15

Abraham, Ralph. On morphodynamics: Selected papers by Ralph Abraham on models for pattern formation processes, morphogenesis, and self-organizing systems showing the evolution of the complex dynamical systems concept over a fifteen year period. Santa Cruz, CA: Aerial Press, 1985.

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16

1960-, Deutsch Andreas, Mukherjee Animesh, and SpringerLink (Online service), eds. Dynamics On and Of Complex Networks: Applications to Biology, Computer Science, and the Social Sciences. Boston: Birkhäuser Boston, 2009.

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17

Karampelas, Panagiotis. Techniques and Tools for Designing an Online Social Network Platform. Vienna: Springer Vienna, 2013.

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18

Ti xi dui kang fu za wang luo jian mo yu fang zhen: SoS-Ops M & S based on the complex network. Beijing: Dian zi gong ye chu ban she, 2010.

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19

Murata, Satoshi. Self-Organizing Robots. Tokyo: Springer Tokyo, 2012.

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20

Large networks and graph limits. Providence, Rhode Island: American Mathematical Society, 2012.

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21

Ozbay, Kaan, and Satish V. Ukkusuri. Advances in Dynamic Network Modeling in Complex Transportation Systems. Springer, 2013.

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22

Ozbay, Kaan, and Satish V. Ukkusuri. Advances in Dynamic Network Modeling in Complex Transportation Systems. Springer, 2015.

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23

Ozbay, Kaan, and Satish V. Ukkusuri. Advances in Dynamic Network Modeling in Complex Transportation Systems. Springer, 2013.

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24

Ozbay, Kaan, and Satish V. Ukkusuri. Advances in Dynamic Network Modeling in Complex Transportation Systems. Springer London, Limited, 2013.

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25

Sonnenschein, Bernard. Collective Dynamics in Complex Networks of Noisy Phase Oscillators: Towards Models of Neuronal Network Dynamics. Logos Verlag Berlin, 2016.

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26

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

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27

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

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28

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

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29

Small, Michael, Xinchu Fu, and Guanrong Chen. Propagation Dynamics on Complex Networks: Models, Methods and Stability. Wiley & Sons, Limited, John, 2014.

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30

Borsboom, Denny. Mental disorders, network models, and dynamical systems. Edited by Kenneth S. Kendler and 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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31

Small, Michael, Xinchu Fu, and Guanrong Chen. Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis. Wiley & Sons, Incorporated, John, 2013.

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32

Small, Michael, Xinchu Fu, and Guanrong Chen. Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis. Wiley & Sons, Incorporated, John, 2013.

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33

Small, Michael, Xinchu Fu, and Guanrong Chen. Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis. Wiley & Sons, Incorporated, John, 2013.

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34

Small, Michael, Xinchu Fu, and Guanrong Chen. Propagation Dynamics on Complex Networks: Models, Methods and Stability Analysis. Wiley, 2014.

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35

Nolte, David D. Flight of the Swallows. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805847.003.0001.

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The science of modern dynamics takes the simple idea of Galileo’s parabolic trajectory and generalizes it into abstract hyperspaces of multiple dimensions. This chapter introduces the new way that physicists and mathematicians visualize dynamical systems, taking a global view of complex behavior and finding that the laws of physics capture the orbits of planets around suns (and the paths of light around black holes) as easily as the evolution of new species or the rise and fall of economies. This new visualization uses phase space to capture the global behavior of complex systems. The path across life, the universe and so many hyperdimensional worlds is being captured by new disciplines within new sciences like chaos theory, entanglement, network science, econophysics and evolutionary dynamics.

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36

Koopmans, Matthijs, and Dimitrios Stamovlasis. Complex Dynamical Systems in Education: Concepts, Methods and Applications. Springer, 2018.

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37

Koopmans, Matthijs, and Dimitrios Stamovlasis. Complex Dynamical Systems in Education: Concepts, Methods and Applications. Springer London, Limited, 2016.

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38

Koopmans, Matthijs, and Dimitrios Stamovlasis. Complex Dynamical Systems in Education: Concepts, Methods and Applications. Springer, 2016.

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39

Kendler, Kenneth S. Introduction to “Mental disorders, network models, and dynamical systems”. Edited by Kenneth S. Kendler and 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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40

Monge, Peter R., and Noshir Contractor. Theories of Communication Networks. Oxford University Press, 2003. http://dx.doi.org/10.1093/oso/9780195160369.001.0001.

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To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic. Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.

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41

Li, Qi, Bo Shen, and Zidong Wang. Control and State Estimation for Dynamical Network Systems with Complex Samplings. Taylor & Francis Group, 2022.

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42

Li, Qi, Bo Shen, and Zidong Wang. Control and State Estimation for Dynamical Network Systems with Complex Samplings. Taylor & Francis Group, 2022.

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43

Li, Qi, Bo Shen, and Zidong Wang. Control and State Estimation for Dynamical Network Systems with Complex Samplings. CRC Press LLC, 2022.

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44

Li, Qi, Bo Shen, and Zidong Wang. Control and State Estimation for Dynamical Network Systems with Complex Samplings. Taylor & Francis Group, 2022.

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45

Li, Qi, Bo Shen, and Zidong Wang. Control and State Estimation for Dynamical Network Systems with Complex Samplings. CRC Press LLC, 2022.

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46

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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47

Graben, Peter, Jürgen Kurths, Changsong Zhou, and Marco Thiel. Lectures in Supercomputational Neuroscience: Dynamics in Complex Brain Networks. Springer London, Limited, 2007.

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48

Nolte, David D. Introduction to Modern Dynamics. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198844624.001.0001.

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Introduction to Modern Dynamics: Chaos, Networks, Space and Time (2nd Edition) combines the topics of modern dynamics—chaos theory, dynamics on complex networks and the geometry of dynamical spaces—into a coherent framework. This text is divided into four parts: Geometric Mechanics, Nonlinear Dynamics, Complex Systems, and Relativity. These topics share a common and simple mathematical language that helps students gain a unified physical intuition. Geometric mechanics lays the foundation and sets the tone for the rest of the book by emphasizing dynamical spaces, like state space and phase space, whose geometric properties define the set of all trajectories through those spaces. The section on nonlinear dynamics has chapters on chaos theory, synchronization, and networks. Chaos theory provides the language and tools to understand nonlinear systems, introducing fixed points that are classified through stability analysis and nullclines that shepherd system trajectories. Synchronization and networks are central paradigms in this book because they demonstrate how collective behavior emerges from the interactions of many individual nonlinear elements. The section on complex systems contains chapters on neural dynamics, evolutionary dynamics, and economic dynamics. The final section contains chapters on metric spaces and the special and general theories of relativity. In the second edition, sections on conventional topics, like applications of Lagrangians, have been strengthened, as well as being updated to provide a modern perspective. Several of the introductory chapters have been rearranged for improved logical flow and there are expanded homework problems at the end of each chapter.

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49

Akulin, Vladimir M. Dynamics of Complex Quantum Systems. Springer London, Limited, 2013.

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50

Dynamics of Complex Quantum Systems. Springer, 2014.

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