Graph Theory and Complex Networks
Graph Theory and Complex Networks

Author: Maarten van Steen

Publisher: Maarten Van Steen

Published: 2010

Total Pages: 285

ISBN-13: 9789081540612

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This book aims to explain the basics of graph theory that are needed at an introductory level for students in computer or information sciences. To motivate students and to show that even these basic notions can be extremely useful, the book also aims to provide an introduction to the modern field of network science. Mathematics is often unnecessarily difficult for students, at times even intimidating. For this reason, explicit attention is paid in the first chapters to mathematical notations and proof techniques, emphasizing that the notations form the biggest obstacle, not the mathematical concepts themselves. This approach allows to gradually prepare students for using tools that are necessary to put graph theory to work: complex networks. In the second part of the book the student learns about random networks, small worlds, the structure of the Internet and the Web, peer-to-peer systems, and social networks. Again, everything is discussed at an elementary level, but such that in the end students indeed have the feeling that they: 1.Have learned how to read and understand the basic mathematics related to graph theory. 2.Understand how basic graph theory can be applied to optimization problems such as routing in communication networks. 3.Know a bit more about this sometimes mystical field of small worlds and random networks. There is an accompanying web site www.distributed-systems.net/gtcn from where supplementary material can be obtained, including exercises, Mathematica notebooks, data for analyzing graphs, and generators for various complex networks.

Air Route Networks Through Complex Networks Theory
Air Route Networks Through Complex Networks Theory

Author: Jose M. Sallan

Publisher: Elsevier

Published: 2019-11-06

Total Pages: 254

ISBN-13: 0128126663

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Air Route Networks through Complex Networks Theory connects theory research with network connectivity analysis, providing practitioners with the tools they need to develop more efficient, resilient and profitable air route networks. The book helps airline route planners and executives create more robust route networks that are less vulnerable to disruptions, such as node isolation. The book further explores errors and attacks in complex networks, strategies for detecting critical nodes and cascading failure models to assess and maximize robustness. The book explains how to measure air route network connectivity with complex network representations. Air transport is among the most dynamic and toughest competition industries in today's global economy. The quality of air route network design is a key strategic factor in an airline's viability. These robust networks provide for more stable and secure carrier operations vs. those based simply on existing supply and demand volumes. Node-specific and network-specific representations are covered, along with in-depth coverage of connectivity in special and temporal networks. These collective tools serve as a guide for practitioners seeking to apply complex network theory to the airline industry. - Presents complex networks theory research results applied to airline transportation networks - Examines airline network robustness in the face of disruptions, providing strategies for detecting critical nodes of air transport networks - Provides historical perspective on the economic, political, technical, and geographical constraints that influence airline route portfolios - Connects data from valuable tools, such as navpoints, area control centers (ACC), and flight information centers, with air network modeling - Studies spreading-related phenomena, such as rumors, and disease contagions, and how these affect the airline industry

Complex Networks
Complex Networks

Author: Vito Latora

Publisher: Cambridge University Press

Published: 2017-09-28

Total Pages: 585

ISBN-13: 1108298680

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Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, mathematics, engineering, biology, neuroscience and the social sciences.

The Structure of Complex Networks
The Structure of Complex Networks

Author: Ernesto Estrada

Publisher: Oxford University Press

Published: 2012

Total Pages: 478

ISBN-13: 019959175X

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The book integrates approaches from mathematics, physics and computer sciences to analyse the organisation of complex networks. Every organisational principle of networks is defined, quantified and then analysed for its influences on the properties and functions of molecular, biological, ecological and social networks.

Complex Network Analysis in Python
Complex Network Analysis in Python

Author: Dmitry Zinoviev

Publisher:

Published: 2018-01-29

Total Pages: 233

ISBN-13: 9781680502695

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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.

Complex Networks
Complex Networks

Author: Vito Latora

Publisher: Cambridge University Press

Published: 2017-09-28

Total Pages: 585

ISBN-13: 1107103185

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A comprehensive introduction to the theory and applications of complex network science, complete with real-world data sets and software tools.

Graph Spectra for Complex Networks
Graph Spectra for Complex Networks

Author: Piet van Mieghem

Publisher: Cambridge University Press

Published: 2010-12-02

Total Pages: 363

ISBN-13: 1139492276

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Analyzing the behavior of complex networks is an important element in the design of new man-made structures such as communication systems and biologically engineered molecules. Because any complex network can be represented by a graph, and therefore in turn by a matrix, graph theory has become a powerful tool in the investigation of network performance. This self-contained 2010 book provides a concise introduction to the theory of graph spectra and its applications to the study of complex networks. Covering a range of types of graphs and topics important to the analysis of complex systems, this guide provides the mathematical foundation needed to understand and apply spectral insight to real-world systems. In particular, the general properties of both the adjacency and Laplacian spectrum of graphs are derived and applied to complex networks. An ideal resource for researchers and students in communications networking as well as in physics and mathematics.

Network Science
Network Science

Author: Albert-László Barabási

Publisher: Cambridge University Press

Published: 2016-07-21

Total Pages: 477

ISBN-13: 1107076269

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Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science.

Complex Systems and Networks
Complex Systems and Networks

Author: Jinhu Lü

Publisher: Springer

Published: 2015-08-14

Total Pages: 483

ISBN-13: 3662478242

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This elementary book provides some state-of-the-art research results on broad disciplinary sciences on complex networks. It presents an in-depth study with detailed description of dynamics, controls and applications of complex networks. The contents of this book can be summarized as follows. First, the dynamics of complex networks, for example, the cluster dynamic analysis by using kernel spectral methods, community detection algorithms in bipartite networks, epidemiological modeling with demographics and epidemic spreading on multi-layer networks, are studied. Second, the controls of complex networks are investigated including topics like distributed finite-time cooperative control of multi-agent systems by applying homogenous-degree and Lyapunov methods, composite finite-time containment control for disturbed second-order multi-agent systems, fractional-order observer design of multi-agent systems, chaos control and anticontrol of complex systems via Parrondos game and many more. Third, the applications of complex networks provide some applicable carriers, which show the importance of theories developed in complex networks. In particular, a general model for studying time evolution of transition networks, deflection routing in complex networks, recommender systems for social networks analysis and mining, strategy selection in networked evolutionary games, integration and methods in computational biology, are discussed in detail.

Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks

Author: Alain Barrat

Publisher: Cambridge University Press

Published: 2012-10-11

Total Pages: 361

ISBN-13: 9781107626256

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The availability of large data sets have allowed researchers to uncover complex properties such as large scale fluctuations and heterogeneities in many networks which have lead to the breakdown of standard theoretical frameworks and models. Until recently these systems were considered as haphazard sets of points and connections. Recent advances have generated a vigorous research effort in understanding the effect of complex connectivity patterns on dynamical phenomena. For example, a vast number of everyday systems, from the brain to ecosystems, power grids and the Internet, can be represented as large complex networks. This new and recent account presents a comprehensive explanation of these effects.