Book description
The Up-to-Date Guide to Complex Networks for Students, Researchers, and Practitioners
Networks with complex and irregular connectivity patterns appear in biology, chemistry, communications, social networks, transportation systems, power grids, the Internet, and many big data applications. Complex Networks offers a novel engineering perspective on these networks, focusing on their key communications, networking, and signal processing dimensions.
Three leading researchers draw on recent advances to illuminate the design and characterization of complex computer networks and graph signal processing systems. The authors cover both the fundamental concepts underlying graph theory and complex networks, as well as current theory and research. They discuss spectra and signal processing in complex networks, graph signal processing approaches for extracting information from structural data, and advanced techniques for multiscale analysis.
What makes networks complex, and how to successfully characterize them
Graph theory foundations, definitions, and concepts
Full chapters on small-world, scale-free, small-world wireless mesh, and small-world wireless sensor networks
Complex network spectra and graph signal processing concepts and techniques
Multiscale analysis via transforms and wavelets
Table of contents
- Cover Page
- About This E-Book
- Title Page
- Copyright Page
- Dedication
- Contents
- Preface
- Acknowledgments
- About the Authors
- About the Cover
-
1 Introduction
- 1.1 Complex Networks
- 1.2 Types of Complex Networks
-
1.3 Benefits of Studying Complex Networks
- 1.3.1 Modeling and Characterizing Complex Physical World Systems
- 1.3.2 Design of New and Efficient Physical World Systems
- 1.3.3 Development of Solutions to Complex Real-World Problems
- 1.3.4 Enhancing Biomedical Research through Molecular Network Modeling
- 1.3.5 Network Medicine
- 1.3.6 Neutralizing Antisocial Networks
- 1.3.7 Enhanced Social Science Research through Social Networks
- 1.4 Challenges in Studying Complex Networks
- 1.5 What This Book Offers
- 1.6 Organization of the Book
- 1.7 Support Materials Available for Instructors
- 1.8 Summary
- 2 Graph Theory Preliminaries
- 3 Introduction to Complex Networks
-
4 Small-World Networks
- 4.1 Introduction
- 4.2 Milgram’s Small-World Experiment
- 4.3 Characteristics of Small-World Networks
- 4.4 Real-World Small-World Networks
- 4.5 Creation and Evolution of Small-World Networks
- 4.6 Capacity-Based Deterministic Addition of New Links
- 4.7 Creation of Deterministic Small-World Networks
- 4.8 Anchor Points in a String Topology Small-World Network
- 4.9 Heuristic Approach-Based Deterministic Link Addition
- 4.10 Routing in Small-World Networks
- 4.11 Capacity of Small-World Networks
- 4.12 Open Research Issues
- 4.13 Summary
- Exercises
-
5 Scale-Free Networks
- 5.1 Introduction
- 5.2 Characteristics of Scale-Free Networks
- 5.3 Real-World Scale-Free Networks
-
5.4 Scale-Free Network Formation
- 5.4.1 Scale-Free Network Creation by Preferential Attachment
- 5.4.2 Scale-Free Network Creation by Fitness-Based Modeling
- 5.4.3 Scale-Free Network Creation by Varying Intrinsic Fitness
- 5.4.4 Scale-Free Network Creation by Optimization
- 5.4.5 Scale-Free Network Creation with Exponent 1
- 5.4.6 Scale-Free Network Creation with Greedy Global Decision Making
- 5.5 Preferential Attachment–Based Scale-Free Network Creation
- 5.6 Fitness-Based Scale-Free Network Creation
- 5.7 Varying Intrinsic Fitness-Based Scale-Free Network Creation
- 5.8 Optimization-Based Scale-Free Network Creation
- 5.9 Scale-Free Network Creation with Exponent 1
- 5.10 Greedy Global Decision–Based Scale-Free Network Creation
- 5.11 Deterministic Scale-Free Network Creation
- 5.12 Open Research Issues
- 5.13 Summary
- Exercises
-
6 Small-World Wireless Mesh Networks
- 6.1 Introduction
- 6.2 Classification of Small-World Wireless Mesh Networks
- 6.3 Creation of Random Long-Ranged Links
- 6.4 Small-World Based on Pure Random Link Addition
- 6.5 Small-World Based on Euclidean Distance
- 6.6 Realization of Small-World Networks Based on Antenna Metrics
- 6.7 Algorithmic Approaches to Create Small-World Wireless Mesh Networks
- 6.8 Gateway-Router-Centric Small-World Network Formation
- 6.9 Creation of Deterministic Small-World Wireless Mesh Networks
- 6.10 Creation of Non-Persistent Small-World Wireless Mesh Networks
- 6.11 Non-Persistent Routing in Small-World Wireless Mesh Networks
- 6.12 Qualitative Comparison of Existing Solutions
- 6.13 Open Research Issues
- 6.14 Summary
- Exercises
-
7 Small-World Wireless Sensor Networks
- 7.1 Introduction
- 7.2 Small-World Wireless Mesh Networks vs. Small-World Wireless Sensor Networks
- 7.3 Why Small-World Wireless Sensor Networks?
- 7.4 Challenges in Transforming WSNs to SWWSNs
- 7.5 Types of Long-Ranged Links for SWWSNs
-
7.6 Approaches for Transforming WSNs to SWWSNs
- 7.6.1 Classification of Existing Approaches
- 7.6.2 Metrics for Performance Estimation
- 7.6.3 Transforming Regular Topology WSNs to SWWSNs
- 7.6.4 Random Model Heterogeneous SWWSNs
- 7.6.5 Newman-Watts Model–Based SWWSNs
- 7.6.6 Kleinberg Model–Based SWWSNs
- 7.6.7 Directed Random Model–Based SWWSNs
- 7.6.8 Variable Rate Adaptive Modulation–Based SWWSNs
- 7.6.9 Degree-Based LL Addition for Creating SWWSNs
- 7.6.10 Inhibition Distance–Based LL Addition for Creating SWWSNs
- 7.6.11 Homogeneous SWWSNs
- 7.7 SWWSNs with Wired LLs
- 7.8 Open Research Issues
- 7.9 Summary
- Exercises
-
8 Spectra of Complex Networks
- 8.1 Introduction
- 8.2 Spectrum of a Graph
- 8.3 Adjacency Matrix Spectrum of a Graph
- 8.4 Adjacency Matrix Spectra of Complex Networks
- 8.5 Laplacian Spectrum of a Graph
- 8.6 Laplacian Spectra of Complex Networks
- 8.7 Network Classification Using Spectral Densities
- 8.8 Open Research Issues
- 8.9 Summary
- Exercises
-
9 Signal Processing on Complex Networks
- 9.1 Introduction to Graph Signal Processing
- 9.2 Comparison between Classical and Graph Signal Processing
- 9.3 The Graph Laplacian as an Operator
- 9.4 Quantifying Variations in Graph Signals
- 9.5 Graph Fourier Transform
- 9.6 Generalized Operators for Graph Signals
- 9.7 Applications
- 9.8 Windowed Graph Fourier Transform
- 9.9 Open Research Issues
- 9.10 Summary
- Exercises
- 10 Graph Signal Processing Approaches
-
11 Multiscale Analysis of Complex Networks
- 11.1 Introduction
- 11.2 Multiscale Transforms for Complex Network Data
- 11.3 Crovella and Kolaczyk Wavelet Transform
- 11.4 Random Transform
- 11.5 Lifting-Based Wavelets
- 11.6 Two-Channel Graph Wavelet Filter Banks
- 11.7 Spectral Graph Wavelet Transform
- 11.8 Spectral Graph Wavelet Transform Based on Directed Laplacian
- 11.9 Diffusion Wavelets
- 11.10 Open Research Issues
- 11.11 Summary
- Exercises
- A Vectors and Matrices
- B Classical Signal Processing
- C Analysis of Locations of Anchor Points
- D Asymptotic Behavior of Functions
- E Relevant Academic Courses and Programs
- F Relevant Journals and Conferences
- G Relevant Datasets and Visualization Tools
- H Relevant Research Groups
- Notation
- Acronyms
- Bibliography
- Index
Product information
- Title: Complex Networks: A Networking and Signal Processing Perspective
- Author(s):
- Release date: February 2018
- Publisher(s): Pearson
- ISBN: 9780134787145
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