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An Introduction to Communication Network Analysis

Author : George Kesidis

Publisher : John Wiley & Sons

Page : 248 pages

File Size : 30,23 MB

Release : 2007-08-24

Category : Computers

ISBN : 0470168676

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Book Description

A self-contained text on modeling and performance evaluation of communication networks This quantitative book focuses on the real issues behind modeling and analysis of communication networks. The author covers a wide variety of topical networking subject matter based on the provided background material in probability, Markov chains, and queues. Leveraging this material, the author explores topics in local multiplexing and routing over three successive chapters, stressing both continuous-time and discrete-time contexts. The remaining chapters focus more directly on networking, such as traffic shaping and multiplexing, static routing, dynamic routing, and peer-to-peer file sharing systems. Providing more rigorous and technically deep coverage than most commonly used networking textbooks, An Introduction to Communication Network Analysis covers classical (e.g., queuing theory) and modern (e.g., pricing) aspects of networking in a clear, accessible manner. Chapters include: * Review of Elementary Probability Theory * Markov Chains * Introduction to Queuing Theory * Local Multiplexing * Queuing Networks with Static Routing * Dynamic Routing with Incentives * Peer-to-Peer File Sharing with Incentives Appendices include additional background information, solutions, and references for selected problems, making this an invaluable text for graduate-level students and networking researchers alike.



Fundamentals of Brain Network Analysis

Author : Alex Fornito

Publisher : Academic Press

Page : 496 pages

File Size : 39,12 MB

Release : 2016-03-04

Category : Medical

ISBN : 0124081185

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Book Description

Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and covers a diverse array of measures for quantifying their topological and spatial organization. It builds intuition for key concepts and methods by illustrating how they can be practically applied in diverse areas of neuroscience, ranging from the analysis of synaptic networks in the nematode worm to the characterization of large-scale human brain networks constructed with magnetic resonance imaging. This text is ideally suited to neuroscientists wanting to develop expertise in the rapidly developing field of neural connectomics, and to physical and computational scientists wanting to understand how these quantitative methods can be used to understand brain organization. - Winner of the 2017 PROSE Award in Biomedicine & Neuroscience and the 2017 British Medical Association (BMA) Award in Neurology - Extensively illustrated throughout by graphical representations of key mathematical concepts and their practical applications to analyses of nervous systems - Comprehensively covers graph theoretical analyses of structural and functional brain networks, from microscopic to macroscopic scales, using examples based on a wide variety of experimental methods in neuroscience - Designed to inform and empower scientists at all levels of experience, and from any specialist background, wanting to use modern methods of network science to understand the organization of the brain



Complex Network Analysis in Python

Author : Dmitry Zinoviev

Publisher : Pragmatic Bookshelf

Page : 330 pages

File Size : 10,6 MB

Release : 2018-01-19

Category : Computers

ISBN : 1680505408

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Book Description

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.



Network Analysis

Author : Ulrik Brandes

Publisher : Springer

Page : 481 pages

File Size : 31,24 MB

Release : 2005-02-02

Category : Computers

ISBN : 3540319557

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Book Description

‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models. From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks. In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.



Theories of Communication Networks

Author : Peter R. Monge

Publisher : Oxford University Press

Page : 431 pages

File Size : 43,65 MB

Release : 2003-03-27

Category : Business & Economics

ISBN : 019803637X

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Book Description

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.



Computational Analysis of Communication

Author : Wouter van Atteveldt

Publisher : John Wiley & Sons

Page : 341 pages

File Size : 33,24 MB

Release : 2022-03-10

Category : Social Science

ISBN : 111968028X

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Book Description

Provides clear guidance on leveraging computational techniques to answer social science questions In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance. Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors—with expertise in both the social sciences and computer science—explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource: Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others Integrates theory, methods, and tools to provide unified approach to the subject Includes sample code in Python and links to actual research questions and cases from social science and communication studies Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science Developed in partnership with the International Communication Association and by the editors of Computational Communication Research Computational Analysis of Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.



Social Network Analysis and Education

Author : Brian V. Carolan

Publisher : SAGE Publications

Page : 345 pages

File Size : 22,96 MB

Release : 2013-03-14

Category : Social Science

ISBN : 1483303519

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Book Description

Social Network Analysis and Education: Theory, Methods & Applications provides an introduction to the theories, methods, and applications that constitute the social network perspective. Unlike more general texts, this applied title is designed for those current and aspiring educational researchers learning how to study, conceptualize, and analyze social networks. Brian V. Carolan's main intent is to encourage you to consider the social network perspective in light of your emerging research interests and evaluate how well this perspective illuminates the social complexities surrounding educational phenomena. Relying on diverse examples drawn from the educational research literature, this book makes explicit how the theories and methods associated with social network analysis can be used to better describe and explain the social complexities surrounding varied educational phenomena.



Handbook of Graphs and Networks in People Analytics

Author : Keith McNulty

Publisher : CRC Press

Page : 266 pages

File Size : 19,3 MB

Release : 2022-06-19

Category : Business & Economics

ISBN : 100059727X

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Book Description

Handbook of Graphs and Networks in People Analytics: With Examples in R and Python covers the theory and practical implementation of graph methods in R and Python for the analysis of people and organizational networks. Starting with an overview of the origins of graph theory and its current applications in the social sciences, the book proceeds to give in-depth technical instruction on how to construct and store graphs from data, how to visualize those graphs compellingly and how to convert common data structures into graph-friendly form. The book explores critical elements of network analysis in detail, including the measurement of distance and centrality, the detection of communities and cliques, and the analysis of assortativity and similarity. An extension chapter offers an introduction to graph database technologies. Real data sets from various research contexts are used for both instruction and for end of chapter practice exercises and a final chapter contains data sets and exercises ideal for larger personal or group projects of varying difficulty level. Key features: Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.



Performance Modeling and Analysis of Communication Networks

Author : Phuoc Tran-Gia

Publisher : BoD – Books on Demand

Page : 370 pages

File Size : 27,72 MB

Release : 2021-10-12

Category : Computers

ISBN : 3958261523

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Book Description

This textbook provides an introduction to common methods of performance modeling and analysis of communication systems. These methods form the basis of traffic engineering, teletraffic theory, and analytical system dimensioning. The fundamentals of probability theory, stochastic processes, Markov processes, and embedded Markov chains are presented. Basic queueing models are described with applications in communication networks. Advanced methods are presented that have been frequently used in recent practice, especially discrete-time analysis algorithms, or which go beyond classical performance measures such as Quality of Experience or energy efficiency. Recent examples of modern communication networks include Software Defined Networking and the Internet of Things. Throughout the book, illustrative examples are used to provide practical experience in performance modeling and analysis. Target group: The book is aimed at students and scientists in computer science and technical computer science, operations research, electrical engineering and economics.



Communication Networks for Smart Grids

Author : Kenneth C. Budka

Publisher : Springer Science & Business Media

Page : 377 pages

File Size : 26,65 MB

Release : 2014-02-06

Category : Computers

ISBN : 1447163028

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Book Description

This book presents an application-centric approach to the development of smart grid communication architecture. The coverage includes in-depth reviews of such cutting-edge applications as advanced metering infrastructure, distribution automation, demand response and synchrophasors. Features: examines a range of exciting utility applications made possible through smart grid evolution; describes the core-edge network architecture for smart grids, introducing the concept of WANs and FANs; explains how the network design paradigm for smart grids differs from that for more established data networks, and discusses network security in smart grids; provides an overview of communication network technologies for WANs and FANs, covering OPGW, PLC, and LTE and MPLS technology; investigates secure data-centric data management and data analytics for smart grids; discusses the transformation of a network from conventional modes of utility operation to an integrated network based on the smart grid architecture framework.