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"This new edition establishes and supports the essential concepts, mathematical framework, analytic resources, and productive skills to confidently address any rational system for quantitative evaluation. However, earlier reliance upon the rigorous mathematical tools developed will now be effectively supported by MATLAB. Coupling the previous editions rigorous mathematical development with MATLAB should permit any rational system to be quantified and evaluated. The author has been granted access to the MATLAB array of products as a participating author using MathWorks technical publications and promoting MATLAB resources"-- Provided by publisher

• Foreword ix Introduction xi
• Part 1. Network Variety and Modeling 1
• Chapter 1. Network Typology 3 1.1. Introduction 3 1.1.1. Network description levels 3 1.1.2. Network, graph and flow 4 1.1.3. Shared or dedicated infrastructure 5 1.1.4. User inclusion 6 1.2. The principal networks 6 1.2.1. (Human) transport networks 6 1.2.2. (Goods) distribution and collection networks 7 1.2.3. Dedicated distribution and collection networks (of fluids and energy) 8 1.2.4. IT networks 9 1.2.5. Communication networks 9 1.2.6. Social and digital social networks 10 1.3. Characterization and typology of networks 11 1.3.1. Key characteristics 11 1.3.2. Network integration 12 1.3.3. Typology 13 1.4. Engineering issues 16 1.5. Performance indicators, evaluation, optimization 18 1.5.1. Performance indicators 18 1.5.2. Evaluation and optimization 20 1.6. Conclusion 23
• Chapter 2. Modeling Discrete Flow Networks 25 2.1. Introduction 25 2.2. Structure 28 2.3. Characterization of a discrete flow 30 2.3.1. Statistical description 30 2.3.2. Probabilistic description 32 2.4. Activities 32 2.5. Control system 37 2.6. Resources 40 2.7. Fluid kinematics 41 2.7.1. Flow/resource/decision synchronization 42 2.7.2. Congestion phenomenon 48 2.7.3. Dissemination of information in social networks 51 2.8. Formalisms for modeling flows in a network 52 2.8.1. BPM tools 53 2.8.2. Timed Petri nets 53 2.8.3. Flow networks 54 2.8.4. Queuing networks 55 2.9. Multi-modeling 57 2.9.1. Multi-formalism versus mono-formalism 57 2.9.2. The DEVS hierarchical model 60 2.9.3. Multi-layer networks 62 2.10. Conclusion 64
• Part 2. Network Analysis Methods and Applications 67
• Chapter 3. Exact Methods Applied to the Flow Analysis of Topological Networks 69 3.1. Introduction 69 3.2. Additive flow networks - deterministic modelling by flow networks 71 3.2.1. Two-terminal series-parallel graph 72 3.2.2. General case - max-flow/min-cut 74 3.3. Additive flow networks - stochastic modelling by queuing networks 76 3.4. Synchronized flow networks - modeling by timed event graphs 81 3.4.1. Steady-state analysis of timed event graphs 81 3.4.2. Example of application: sizing a flow-shop 83 3.5. Conclusion 88
• Chapter 4. Simulation Techniques Applied to the Analysis of Sociological Networks 91 4.1. Introduction 91 4.2. Simulation techniques 92 4.2.1. Discrete event simulation (worldviews) 94 4.2.2. DEVS formalism 96 4.2.3. Coupling simulation/resolutive methods 100 4.2.4. Distributed simulation 102 4.2.5. Architectural solutions 103 4.2.6. Time management and synchronization 104 4.2.7. Pessimistic approach 104 4.2.8. Optimistic approach 105 4.2.9. HLA 106 4.2.10. Cosimulation 107 4.2.11. FMI/FMU 108 4.2.12. FMI/FMU and HLA coupling 109 4.3. Simulation of flows in sociological networks 110 4.3.1. Behavioral simulation based on DEVS formalism 111 4.3.2. Application study 113 4.4. Conclusion 116
• Part 3. Case Studies 119
• Chapter 5. Smart Grid 121 5.1. Summary of the study 122 5.2. Demand profile 122 5.3. Solar power station, fuel station and regional import 123 5.4. Hydroelectric power station and PHES 123 5.5. Operational issues 124 5.6. Model 125 5.6.1. Decision variables 125 5.6.2. Constraints 126 5.6.3. Objective function 127 5.7. Optimization results 128
• Chapter 6. Forestry Logistics 131 6.1. Summary of the study 132 6.2. Forest timber supply problem 132 6.3. Tactical planning model 134 6.4. Logistics benchmarking 136 6.4.1. AS IS scenario (non-collaborative logistics) 136 6.4.2. TO BE scenario (collaborative logistics) 137 6.4.3. Results 138 6.5. Conclusion 139
• Chapter 7. Multi-layered Digital Social Networks 143 7.1. Summary of the study 144 7.2. Digital social networks 144 7.3. Studying digital social networks via an interview broadcast 145 7.3.1. Pre-interview social network scenario 146 7.3.2. Social network audience 148 7.4. Modeling and simulation 148 7.4.1. Modeling the interview production and broadcast processes 148 7.4.2. MSN/HLA simulation architecture 149 7.5. Simulation results 152 7.6. Conclusion and perspectives 154 References 157 Index 167.
• (source: Nielsen Book Data)

As a result of its widespread implementation in economic and social structures, the network concept appears to be a paradigm of the contemporary world. The need for various services - transport, energy, consumption of manufacturing goods, provision of care, information and communication, etc. - draws users into interwoven networks which are meshes of material and immaterial flows. In this context, the user is a consumer of goods and services from industries and administrations, or they themselves are part of the organization (digital social networks). This book examines the invariants that unify networks in their diversity, as well as the specificities that differentiate them. It provides a reading grid that distinguishes a generic level where these systems find a common interpretation, and a specific level where appropriate analytical methods are used. Three case studies from different fields are presented to illustrate the purpose of the book in detail.
(source: Nielsen Book Data)

• Front Matter
• Network Variety and Modeling. Network Typology
• Modeling Discrete Flow Networks
• Network Analysis Methods and Applications. Exact Methods Applied to the Flow Analysis of Topological Networks
• Simulation Techniques Applied to the Analysis of Sociological Networks
• Case Studies. Smart Grid
• Forestry Logistics
• Multi-layered Digital Social Networks
• References
• Index
• Other titles from iSTE in Systems and Industrial Engineering - Robotics

• 1. Introduction
• 2.Characteristics of grey system theory
• 3.Grey Numbers and their Operations
• 4. Sequence Operators and Grey Data Mining
• 5. Grey Relational Analysis Models
• 6. Grey Clustering Evaluation Models
• 7. Series of GM Models
• 8. Combined Grey Models
• 9.Techniques for Grey System Forecasting
• 10. Grey Models for Decision-Making
• 11. Grey Control Systems
• 12. Spectrum analysis of sequence operators. .

The book covers up-to-date theoretical and applied advances in grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the authors, including simplified form of grey number, general grey number and the operations of grey numbers; the axiomatic system of buffer operators and a series of weakening and strengthening operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative and three dimension degree, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute intelligent grey target decision models, weight vector group with kernel and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. This book will be appropriate as a reference and/or professional book for courses of grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, medicine, astronomy, earth science, economics, and management. It can also be utilized by researchers and practitioners in research institutions, business entities, and government agencies. .
(source: Nielsen Book Data)

• 1. Introduction
• 2. Weak multiplex percolation
• 3. Relation to other percolation models
• 4. Critical phenomena with rapidly decaying degree distributions
• 5. Critical phenomena with broad degree distributions
• 6. Conclusions
• References.
• (source: Nielsen Book Data)

In many systems consisting of interacting subsystems, the complex interactions between elements can be represented using multilayer networks. However percolation, key to understanding connectivity and robustness, is not trivially generalised to multiple layers. This Element describes a generalisation of percolation to multilayer networks: weak multiplex percolation. A node belongs to a connected component if at least one of its neighbours in each layer is in this component. The authors fully describe the critical phenomena of this process. In two layers with finite second moments of the degree distributions the authors observe an unusual continuous transition with quadratic growth above the threshold. When the second moments diverge, the singularity is determined by the asymptotics of the degree distributions, creating a rich set of critical behaviours. In three or more layers the authors find a discontinuous hybrid transition which persists even in highly heterogeneous degree distributions, becoming continuous only when the powerlaw exponent reaches $1+1/(M-1)$ for $M$ layers.
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After the invention of a new mechanical element called 'inerter' in 2002, research interest in passive network synthesis has been revived and this field has again become active and essential.The unique compendium highlights the synthesis of passive electrical or mechanical networks, which is motivated by the vibration control based on a new type of mechanical elements named inerter. It introduces important fundamental concepts of passive network synthesis, and presents recent results on this topic.These new results concern mainly the economical realizations of low-degree functions as RLC networks (damper-spring-inerter networks), the synthesis of n-port resistive networks, and the synthesis of low-complexity mechanical networks. They can be directly applied to the optimization and design of various inerter-based mechanical control systems, such as suspension systems, vibration absorbers, building vibration systems, etc.This useful reference text provides important methodologies and results for researchers in the fields of circuit theory, vibration system control, passive systems, control theory, and electrical engineering.
(source: Nielsen Book Data)

• 1.Business Policy and Strategy, System Dynamics Applications to.- 2.Delay and Disruption in Complex Projects.- 3.Diffusion of Innovations, System Dynamics Analysis of the.- 4.Dynamics of Income Distribution in a Market Economy: Possibilities for Poverty Allevation.- 5.Group Model Building.- 6.Health Care in the United Kingdom and Europe, System Dynamics Applications to.- 7.Health Care in the United States, System Dynamics Applications to.- 8.Management of Renewable Resources, System Dynamics in the.- 9.Public Policy, System Dynamics Applications to.- 10.Scenario-Driven Planning with System Dynamics.- 11.System Dynamics and Its Contribution to Economics and Economic Modeling.- 12.System Dynamics and Organizational Learning.- 13.System Dynamics in the Evolution of the Systems Approach.- 14.System Dynamics Modeling: Validation for Quality Assurance.- 15.System Dynamics Models of Environment, Energy and Climate Change.- 16.System Dynamics Models, Optimization of.- 17.System Dynamics Philosophical Background and Underpinnings.- 18.System Dynamics, Analytical Methods for Structural Dominance Analysis in.- 19.System Dynamics, Introduction to.- 20.System Dynamics, The Basic Elements of.-
• 21. System Dynamics in Defence.- 22.System Dynamics and Project Management.- 23.System Dynamics and Workforce Planning.- 24.The Engineering of Strategy.- 25.System Dynamics and Operations Management Index.
• (source: Nielsen Book Data)

This new book addresses the status of the field of System Dynamics 60+ years after its inception. It presents state-of-the-art expositions by leading authorities in either a facet of the theory and methodology of the subject or its application in a specific domain. Exhibiting greater reach and authority than would be possible in a conventional authored textbook, the volume includes nine chapters covering methodological aspects, and 14 on various contemporary applications. Emerging from the System Dynamics section of the Encyclopedia of Complexity & Systems Science, First Edition (2009), the book features brand new chapters covering project management, workforce modelling, applications in defense, operations management, engineering of strategy, the roots of model validation, as well as many considerably enhanced versions of existing chapters. Together, the chapters reveal a remarkable landscape of theory and practice, and how System Dynamics can contribute critical policy insights to a broad audience of students and professionals across many fields of study. .
(source: Nielsen Book Data)

• Preface.- Introduction (Francesca Biagini, Goeran Kauermann, Thilo Meyer-Brandis).- Network Visualization (Ulrik Brandes and Michael Sedlmair).- A Statistician's View of Network Modeling (David R. Hunter).- The Rank-one and the Preferential Attachment Paradigm (Steffen Dereich).- Systemic Risk in Networks (Nils Detering, Thilo Meyer-Brandis, Konstantinos Panagiotou, Daniel Ritter).- Bayesian Networks for Max-linear Models (Claudia Kluppelberg, Steffen Lauritzen).- Introduction to Network Inference in Genomics (Ernst C. Wit).
• (source: Nielsen Book Data)

This book provides an overview of network science from the perspective of diverse academic fields, offering insights into the various research areas within network science. The authoritative contributions on statistical network analysis, mathematical network science, genetic networks, Bayesian networks, network visualisation, and systemic risk in networks explore the main questions in the respective fields: What has been achieved to date? What are the research challenges and obstacles? What are the possible interconnections with other fields? And how can cross-fertilization between these fields be promoted? Network science comprises numerous scientific disciplines, including computer science, economics, mathematics, statistics, social sciences, bioinformatics, and medicine, among many others. These diverse research areas require and use different data-analytic and numerical methods as well as different theoretical approaches. Nevertheless, they all examine and describe interdependencies, associations, and relationships of entities in different kinds of networks. The book is intended for researchers as well as interested readers working in network science who want to learn more about the field - beyond their own research or work niche. Presenting network science from different perspectives without going into too much technical detail, it allows readers to gain an overview without having to be a specialist in any or all of these disciplines.
(source: Nielsen Book Data)

• Multivariate Polynomial Fundamentals for Multidimensional Systems.- Multivariate Polynomial Positivity (Nonnegativity) Tests.- Multidimensional Sampling.- Multidimensional Digital Filter Recursibility and Stability.- 2-D Fir Filters, Linear Prediction adn 2-D IIR Filters.- Wavelets and Filter Banks.
• (source: Nielsen Book Data)

Revised and updated, this concise new edition of the pioneering book on multidimensional signal processing is ideal for a new generation of students. Multidimensional systems or m-D systems are the necessary mathematical background for modern digital image processing with applications in biomedicine, X-ray technology and satellite communications. Serving as a firm basis for graduate engineering students and researchers seeking applications in mathematical theories, this edition eschews detailed mathematical theory not useful to students. Presentation of the theory has been revised to make it more readable for students, and introduce some new topics that are emerging as multidimensional DSP topics in the interdisciplinary fields of image processing. New topics include Groebner bases, wavelets, and filter banks.
(source: Nielsen Book Data)

As network science and technology continues to gain popularity, it becomes imperative to develop procedures to examine emergent network domains, as well as classical networks, to help ensure their overall optimization. Advanced Methods for Complex Network Analysis features the latest research on the algorithms and analysis measures being employed in the field of network science. Highlighting the application of graph models, advanced computation, and analytical procedures, this publication is a pivotal resource for students, faculty, industry practitioners, and business professionals interested in theoretical concepts and current developments in network domains.
(source: Nielsen Book Data)

Networks are involved in many aspects of everyday life, from food webs in ecology and the spread of pandemics to social networking and public transport. This book explores the basics of network theory to understand the science of complexity and its importance, using examples from nature, technology, and society, and history

• Preface.- Entropy of Digraphs and Infinite Networks.- An Information-Theoretic Upper Bound on Planar Graphs Using Well-orderly Maps.- Probabilistic Inference Using Function Factorization and Divergence Minimization.- Wave Localization on Complex Networks.- Information-Theoretic Methods in Chemical Graph Theory.- On the Development and Application of Net-Sign Graph Theory.- The Central Role of Information Theory in Ecology.- Inferences About Coupling from Ecological Surveillance Monitoring.- Markov Entropy Centrality.- Social Ontologies as Generalizedd Nearly Acyclic Directed Graphs.- Typology by Means of Language Networks.- Information Theory-Based Measurement of Software.- Fair and Biased Random Walks on Undirected Graphs and Related Entropies.
• (source: Nielsen Book Data)

For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks. This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. As such, it marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines and can serve as a valuable resource for a diverse audience of advanced students and professional scientists. While it is primarily intended as a reference for research, the book could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.
(source: Nielsen Book Data)

• 1. Introduction
• Part I. Random Network Models: 2. The Erdos-Renyi models
• 3. Observations in real-world networks
• 4. Models for complex networks
• 5. Growing network models
• Part II. Structure and Robustness of Complex Networks: 6. Distances in scale-free networks - the ultra small world
• 7. Self-similarity in complex networks
• 8. Distances in geographically embedded networks
• 9. The network's structure - the generating function method
• 10. Percolation on complex networks
• 11. Structure of random directed networks - the bow tie
• 12. Introducing weights - bandwidth allocation and multimedia broadcasting
• Part III. Network Function - Dynamics and Applications: 13. Optimization of the network structure
• 14. Epidemiological models
• 15. Immunization
• 16. Thermodynamic models on networks
• 17. Spectral properties, transport, diffusion and dynamics
• 18. Searching in networks
• 19. Biological networks and network motifs
• Part IV. Appendices
• References
• Index.
• (source: Nielsen Book Data)

Examining important results and analytical techniques, this graduate-level textbook is a step-by-step presentation of the structure and function of complex networks. Using a range of examples, from the stability of the internet to efficient methods of immunizing populations, and from epidemic spreading to how one might efficiently search for individuals, this textbook explains the theoretical methods that can be used, and the experimental and analytical results obtained in the study and research of complex networks. Giving detailed derivations of many results in complex networks theory, this is an ideal text to be used by graduate students entering the field. End-of-chapter review questions help students monitor their own understanding of the materials presented.
(source: Nielsen Book Data)

• Preface.A Personal Note from William T. Scherer.A Personal Note from William F. Gibson.A Personal Note from Scott F. Ferber.Original Preface from Jack Gibson.Acknowledgments
• .1. Introduction.1.1 What is a System?1.2 Terminology Confusion.1.3 Systems Analysis Equals Operations Research Plus Policy Analysis.1.4 Attributes of Large-Scale Systems.1.5 Intelligent Transportation Systems (ITS): An Example of a Large-Scale System.1.6 Systems Integration.1.7 What Makes a "Systems Analysis" Different?1.8 Distant Roots of Systems Analysis.1.9 Immediate Precursors to Systems Analysis.1.10 Development of Systems Analysis As a Distinct Discipline: The Influence of RAND.Historical Case Study: IIASA (A).Exercises.Case Study: Fun at Six Flags?Historical Case Study: IIASA (B)
• .2. Six Major Phases of Systems Analysis.2.1 The System Analysis Method: Six Major Phases.2.2 The Goal-Centered or Top-Down Approach.2.3 The Index of Performance Concept.2.4 Developing Alternative Scenarios.2.5 Ranking Alternatives.2.6 Iteration and the "Error-Embracing" Approach.2.7 The Action Phase: The Life-Cycle of a System.Exercises.Case Study: Methodologies or Chaos? Part A.Case Study: Methodologies or Chaos? Part B.Case Study: Wal-Mart Crisis
• !3. Goal Development.3.1 Seven Steps in Goal Development.3.2 On Generalizing the Question.3.3 The Descriptive Scenario.3.4 The Normative Scenario.3.5 The Axiological Component.3.6 Developing an Objectives Tree.3.7 Fitch's Goals for Urbanizing America: An Example of Objectives Tree Construction.3.8 Content Analysis of Fitch's Goals.3.9 Validate.3.10 Iterate.Case Study: Distance Learning in the Future?Historical Case Study: Goals of 4C, Inc
• .4. The Index of Performance.4.1 Introduction.4.2 Desirable Characteristics for an Index of Performance.4.3 Economic Criteria.4.4 Compound Interest.4.5 Four Common Criteria for Economic Efficiency.4.6 Is there a Problem with Multiple Criteria?4.7 What is Wrong with the B-C Ratio?4.8 Can IRR be Fixed?4.9 Expected Monetary Value.4.10 Nonmonetary Performance Indices.Exercises.Case Study: Sky High AirlinesCase Study: Bridges-Where to Spend the Security Dollars?Case Study: Measuring the Process and Outcomes of Regional Transportation Collaboration.Case Study: Baseball Free Agent Draft
• .5. Develop Alternative Candidate Solutions.5.1 Introduction.5.2 The Classical Approach to Creativity.5.3 Concepts in Creativity.5.4 Brainstorming.5.5 Brainwriting.5.6 Dynamic Confrontation.5.7 Zwicky's Morphological Box.5.8 The Options Field/Options Profile Approach.5.9 Computer Creativity.5.10 Computer Simulation: a Tool in Option Development.5.11 Why a Dynamic Simulation for Creating Options?5.12 Context-Free Simulation Models?5.13 Bottom-Up Simulation or Top-Down?5.14 Lessons from the Susquehanna River Basin Model.5.15 The Forrester Urban Model (FUM) and Societal Values.5.16 Extensions and Variations.5.17 Where to go from Here?Exercises.Case Study: Winnebago.Case Study: Distance Learning in the Future?Historical Case Study: Real-Time Television Link with Mars Orbiter.Historical Case Study: A Highway Vehicle Simulator.RFP from DOT
• .6. Rank Alternative Candidates.6.1 Introduction.6.2 Rating and Ranking Methods.6.3 Condorcet and Arrow Voting Paradoxes.6.4 A MultiStage Rating Process.6.5 Decision Analysis.6.6 Basic Axioms of Decision Theory.6.7 Properties of Utility Functions.6.8 Constructing a Utility Curve.6.9 Some Decision Analysis Classic Examples.6.10 Estimation Theory in Decision Analysis.6.11 Some Practical Problems with Decision Analysis. 6.12 Practical Trade Studies.Exercises.Case Study: Training Center Location.Case Study: Corporate Headquarters Location.Case Study: Business School Selection
• .7. Iteration and Transition.7.1 Iteration.7.2 Segment and Focus.7.3 The Transition Scenario.7.4 The Gantt Chart.7.5 Interaction Matrices.7.6 The Delta Chart.7.7 The Audit Trail.7.8 Cost of Failure to Stay on Schedule.7.9 Responsibilities of Major Actors.7.10 Sign-Offs by Cooperating Groups.Exercises
• .8. Management of the Systems Team.8.1 Introduction.8.2 Personal Style in an Interdisciplinary Team.8.3."Out-Scoping" and "In-Scoping" in a System Study.8.4 Building the Systems Team.8.5 Tips on Managing the Team.8.6 Functional or Project Management?8.7 How to Make an Effective Oral Presentation.8.8 How to Write a Report
• .9. Project Management.9.1 Introduction.9.2 Project Management Versus Process Management.9.3 The Hersey-Blanchard Four-Mode Theory.9.4 Relation of Management Style to Project Management.9.5 Preliminary Project Planning.9.6 Dealing with Conflict in Project Management.9.7 Life-Cycle Planning and Design.9.8 PERT/CPM Program Planning Method: An Example.9.9 Quality Control in Systems Projects.Case Study: Project Management
• .10. The 10 Golden Rules of Systems Analysis.10.1 Introduction.10.2 Rule
• 1: There Always Is a Client.10.3 Rule
• 2: Your Client Does Not Understand His Own Problem.10.4 Rule
• 3: The Original Problem Statement is too Specific: You Must Generalize the Problem to Give it Contextual Integrity.10.5 Rule
• 4: The Client Does Not Understand the Concept of the Index of Performance.10.6 Rule
• 5: You are the Analyst, Not the Decision-Maker.10.7 Rule
• 6: Meet the Time Deadline and the Cost Budget.10.8 Rule
• 7: Take a Goal-Centered Approach to the Problem, Not a Technology-Centered or Chronological Approach .10.9 Rule
• 8: Nonusers Must be Considered in the Analysis and in the Final Recommendations.10.10 Rule
• 9: The Universal Computer Model is a Fantasy.10.11 Rule
• 10: The Role of Decision-Maker in Public Systems is Often a Confused One.References.Index.
• (source: Nielsen Book Data)

This book focuses on systems analysis, broadly defined to also include problem formulation and interpretation of proposed alternatives in terms of the value systems of stakeholders. Therefore, the book is a complement, not a substitute to other books when teaching systems engineering and systems analysis. The nature of problem solving discussed in this book is appropriate to a wide range of systems analyses. Thus the book can be used as a stand-alone book for teaching the analysis of systems. Also unique is the inclusion of broad case studies to stress problem solving issues, making "How to Do Systems Analysis" a complement to the many fine works in systems engineering available today.
(source: Nielsen Book Data)

• Preliminaries and Basic Definitions in Network Theory
• Models of Complex Networks
• Correlations in Complex Networks
• The Architecture of Complex Weighted Networks
• Community Structure Identification
• Visualizing Large Complex Networks
• Modeling the Webgraph: How Far We Are
• The Large Scale Structure of the Internet
• Spanning Tree in Ecology
• Social and Financial Networks.
• (source: Nielsen Book Data)

This book is the culmination of three years of research effort on a multidisciplinary project in which physicists, mathematicians, computer scientists and social scientists worked together to arrive at a unifying picture of complex networks. The contributed chapters form a reference for the various problems in data analysis visualization and modeling of complex networks.
(source: Nielsen Book Data)