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Книги з теми "Network module"

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Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Group, Automotive Training. Network diagnostics & module programming. San Diego, C.A: ATG, Inc., 2010.

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2

1959-, Moran Diane, ed. ECDL module 7. London: Springer, 2000.

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3

Anderson, Mary L. Design and implementation of a token-ring optic local area network interface module. Monterey, Calif: Naval Postgraduate School, 1989.

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4

University of Kent at Canterbury. Department of Information Technology. The learning at work project: Work-based module : network systems management : student's journal. Canterbury: University of Kent at Canterbury, 1994.

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5

Green, Samantha J. Cost effectiveness analysis of converting a classroom course to a network based instruction module. Monterey, Calif: Naval Postgraduate School, 1997.

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6

Odvina, Cesar A. An analysis of the initial decision process of organizing the Navy Medical Department's Executive Management Education module conversion to network-based instruction. Monterey, Calif: Naval Postgraduate School, 1998.

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7

Arvind, Durai, ed. Cisco secure firewall services module (FWSM). Indianapolis, IN: Cisco Press, 2009.

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8

Advances in transport network technologies: Photonic networks, ATM, and SDH. Boston: Artech House, 1996.

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9

Healey, Steven Thomas. Abstract partitioning and routing of logic networks for custom module generation. Urbana, Ill. (1304 W. Springfield Ave., Urbana 61801-2987): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1987.

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10

Topór-Kamiński, Lesław. Bezinercyjne elementy osobliwe jako modele elektrycznych układów aktywnych. Gliwice: Wydawn. Politechniki Śląskiej, 1996.

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11

Agha, Khaldoun Al. Network coding. Hoboken, NJ: Wiley-ISTE, 2012.

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12

An engineering approach to computer networking: ATM networks, the internet, and the telephone network. Reading, Mass: Addison-Wesley, 1997.

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13

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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Анотація:

The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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14

Kosecki, Andrzej. Stochastyczne modele sieciowe przy planowaniu zadań budowlanych. Kraków: Politechnika Krakowska, 1989.

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15

Jain, Bijendra N. Open systems interconnection: Its architecture and protocols. Amsterdam: Elsevier, 1990.

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16

Jain, Bijendra N. Open systems interconnection: Its architecture and protocols. New York: McGraw-Hill, 1993.

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17

Wynants, Christelle. Network synthesis problems. Dordrecht: Kluwer Academic Publishers, 2001.

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18

Wynants, Christelle. Network synthesis problems. Dordrecht: Kluwer Academic Publishers, 2001.

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19

Krishna, Chepuri Shri. Network economies in Indian telecom. Ahmedabad: Indian Institute of Management, 2007.

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20

1967-, Levinson David M., ed. Evolving transportation networks. New York: Springer, 2011.

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21

Stoer, Mechthild. Design of survivable networks. Berlin: Springer-Verlag, 1992.

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22

Neural network principles. Englewood Cliffs, NJ: Prentice Hall, 1994.

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23

Harvey, Robert L. Neural network principles. London: Prentice-Hall International, 1994.

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24

service), SpringerLink (Online, ed. Pro Spring Dynamic Modules for OSGi™ Service Platforms. Berkeley, CA: Apress, 2009.

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25

The economic analysis of the growth of network products: The case of interorganizational systems. Frankfurt am Main: Lang, 1998.

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26

Handbook on biological networks. Hackensack, NJ: World Scientific, 2010.

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27

Koch, Thorsten. Evaluating gas network capacities. Philadelphia: Society for Industrial and Applied Mathematics, 2015.

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28

ATM network performance. Boston: Kluwer Academic, 1996.

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29

ATM network performance. 2nd ed. Boston: Kluwer Academic, 2000.

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30

Shmulevich, Ilya. Probabilistic boolean networks: The modeling and control of gene regulatory networks. Philadelphia: Society for Industrial and Applied Mathematics, 2010.

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31

United States. Naval Education and Training Command, ed. Radioman Training Series, Module 3-Network Communications, Training Manual (Traman) And Nonresident Training Course (NRTC), October 1997. [S.l: s.n., 1998.

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32

An Analysis of the Initial Decision Process of Organizing the Navy Medical Departments Executive Management Education Module Conversion to Network- Based Instruction. Storming Media, 1998.

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33

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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Анотація:

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 frameworks are introduced including random graphs, growing network models (including notably the Barabasi–Albert Model) and network ensembles.

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34

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

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Анотація:

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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35

Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

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Анотація:

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

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36

Newman, Mark. Models of network formation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0013.

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Анотація:

This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.

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37

Ad-hoc Networks: Fundamental Properties and Network Topologies. Springer, 2006.

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38

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. Finally, the rich interplay between structure and function in multilayer networks is discussed in the framework of game theory.

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39

Snijders, Tom A. B., and Mark Pickup. Stochastic Actor Oriented Models for Network Dynamics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.10.

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Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.

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40

Umar, Amjad. E-Business and Distributed Systems Handbook: Networks Module. WWW.Amjadumar.com, 2003.

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41

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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Анотація:

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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42

Newman, Mark. Epidemics on networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0016.

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Анотація:

This chapter discusses the spread of diseases over contact networks between individuals and the methods used to model this process. The chapter begins with an introduction to the classic models of mathematical epidemiology, including the SI model, the SIR model, and the SIS model. Models for coinfection and competition between diseases are also discussed, as well as “complex contagion” models used to represent the spread of information. The remainder of the chapter deals with the behavior of these models on networks, where the behavior of spreading diseases depends strongly on network structure. It is shown that the SIR model maps to a bond percolation process on networks, allowing us to solve for static properties such as the total number of individuals infected in a disease outbreak. The case of the configuration model is developed in detail and the calculations are extended to competing diseases, coinfection, and complex contagion. Time-dependent behavior of diseases on networks is also studied using various differential equation approximations, including pair approximations and degree-based approximations.

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43

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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Анотація:

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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44

Newman, Mark. Percolation and network resilience. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0015.

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A discussion of the site percolation process on networks and its application as a model of network resilience. The chapter starts with a description of the percolation process, in which nodes are randomly removed from a network, and of the percolation phase transition at which a giant percolating cluster forms. The properties of percolation on configuration model networks are studied, including networks with power-law degree distributions, and including both uniform and non-uniform removal of nodes. Computer algorithms for simulating percolation on real-world networks are also discussed, and numerical results are given for several example networks, including the internet and a social network.

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45

Patty, John W., and Elizabeth Maggie Penn. Network Theory and Political Science. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.12.

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Анотація:

This chapter considers the role of network theory in the study of political phenomena, the analytical theoretical basis of network analysis as applied in political science. Using the concepts of centrality, community, and connectivity, it discusses the relationship between the primitives of network theory and their relationship to empirical measurement of political networks. The chapter then discusses one of the most active areas of work on network theory in political science, models of network formation, and offers some concluding thoughts about future directions of network theory in political science. We argue that the deeper theorizing about political networks will complement and improve empirical scholarship on the role of networks in politics.

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46

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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Анотація:

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 network. It displays a rich phase diagram including both continuous and discontinuous phase transitions. Antagonist percolation on a duplex network describes the competition between two layers and can give rise to hysteresis loops corresponding to phases that either one layer or the other can percolate Avalanches generated by the generalized Sandpile Model and Watts–Strogatz Model are also discussed, emphasizing their relevance for studying the stability of power grids and financial systems.

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47

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Network growth algorithms. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0008.

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Анотація:

Growth processes are a fundamentally different approach compared to probability-driven exponential models covered in earlier chapters. This chapter studies how growth rules can be designed to mimic processes observed in the real world, and how the process can be mathematically analyzed in order to obtain information about the likely topological properties of the resulting networks. The configuration (stub joining) model is described, including a careful discussion of how bias can be introduced if backtracking is used instead of restarting if stubs join to form a self or double link. The second class of models looked at is preferential attachment. The simplest variants of this are analyzed with a master equation approach, in order to introduce this technique as a way of obtaining analytical information about the expected properties of the generated graphs. Extensive references are provided to the numerous variants and extensions of both of these models.

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48

Dorff, Cassy, Shahryar Minhas, and Michael D. Ward. Latent Networks and Spatial Networks in Politics. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.11.

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Анотація:

Network analysis is a growing field in political science, with topics ranging from the study of individual actors in congressional networks to international war between countries. This chapter briefly summarizes the history of network analysis, the barriers facing previous approaches, and current innovations, with an emphasis on latent variable approaches. These approaches provide an organic link to the consideration of spatial networks, also discussed in detail. These innovations expand researchers’ ability to capture the many different facets of network-motivated questions, including how networks evolve or how spatial proximity determines network ties. The chapter concludes with a brief comparison of two major types of latent variable models and their relation to other network approaches commonly used in political science.

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49

Newman, Mark. Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.001.0001.

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Анотація:

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in recent years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyse network data on an unprecendented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadly interdisciplinary and developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social science. This book brings together the most important breakthroughts in each of these fields and presents them in a unified fashion, highlighting the strong interconnections between work in different areas. Topics covered include the measurement of networks; methods for analysing network data, including methods developed in physics, statistics, and sociology; fundamentals of graph theory; computer algorithms, including spectral algorithms and community detection; mathematical models of networks such as random graph models and generative models; and models of processes taking place on networks.

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50

Yust, Jason. Structural Networks and the Experience of Musical Time. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190696481.003.0005.

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Анотація:

The network model of temporal structure allows for many generalized concepts of musical time that can be applied across different modalities (rhythmic, tonal, and formal). This chapter defines network depths, distances, paths, centers, skew, and bias, and partially classifies network types such as piles, tortoises, and starfish. A splitting operation on networks is defined and applied to the problem of relating networks in different modalities and finding true disjunctions.

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