Description
A comprehensive look at the emerging science of networks Network science helps you design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems; and analyze social interactions among people. This is the first book to take a comprehensive look at this emerging science. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. The book’s uniqueness lies in its integration of concepts across computer science, biology, physics, social network analysis, economics, and marketing. The book is divided into easy-to-understand topical chapters and the presentation is augmented with clear illustrations, problems and answers, examples, applications, tutorials, and a discussion of related Java software. Chapters cover: * Origins * Graphs * Regular Networks * Random Networks * Small-World Networks * Scale-Free Networks * Emergence * Epidemics * Synchrony * Influence Networks * Vulnerability * Net Gain * Biology This book offers a new understanding and interpretation of the field of network science. It is an indispensable resource for researchers, professionals, and technicians in engineering, computing, and biology. It also serves as a valuable textbook for advanced undergraduate and graduate courses in related fields of study. 1. ORIGINS. 1.1 What is Network Science?. 1.2 A Brief History of Network Science. 1.3 General Principles. 2. GRAPHS. 2.1 Set Theoretical Definition of a Graph. 2.2 Matrix Algebra Definition of a Graph. 2.4 Spectral Properties of Graphs. 2.5 Types of Graphs. 2.6 Topological Structure. 2.7 Graphs in Software. 2.8. Exercises. 3. REGULAR NETWORKS. 3.1 Diameter, Centrality, and Average Path Length. 3.2 Binary Tree Network. 3.3 Toroidal Network. 3.4 Hypercube Networks. 3.5 Exercises. 4. RANDOM NETWORKS. 4.1 Generation of Random Networks. 4.2 Degree Distribution of Random Networks. 4.3 Entropy of Random Networks. 4.4 Diameter, Centrality, and Closeness in Random Networks. 4.5. Weak Ties in Random Networks. 4.6 Randomization of Regular Networks. 4.7 Analysis. 4.8 Exercises. 5. SMALL WORLD NETWORKS. 5.1 Generating a Small World Network. 5.2 Properties of Small World Networks. 5.3 Phase Transition. 5.4 Navigating Small Worlds. 5.5 Weak Ties in Small World Networks. 5.6 Analysis. 5.7 Exercises. 6. SCALE FREE NETWORKS. 6.1 Generating a Scale-Free Network. 6.2 Properties of Scale-Free Networks. 6.3 Navigation in Scale-Free Networks. 6.4 Analysis. 6.5 Exercises. 7. EMERGENCE. 7.1 What is Network Emergence?. 7.2 Emergence in the Sciences. 7.3 Genetic Evolution. 7.4 Designer Networks. 7.5 Permutation Network Emergence. 7.6 An Application of Emergence. 7.7 Exercises. 8. EPIDEMICS. 8.1. Epidemic Models. 8.2 Persistent Epidemics in Networks. 8.3 Network Epidemic Simulation Software. 8.4 Countermeasures. 8.5 Exercises. 9. SYNCHRONY. 9.1 To Sync or Not To Sync. 9.2 A Cricket Social Network. 9.3 Kirchhoff Networks. 9.4 Anatomy of Buzz. 9.5 Exercises. 10. INFLUENCE NETWORKS. 10.1 Anatomy of Buzz. 10.2 Power in Social Networks. 10.3 Conflict in I-nets. 10.4 Command Hierarchies. 10.5 Emergent Power in I-nets. 10.6 Exercises. 11. VULNERABILITY. 11.1 Network Risk. 11.2 Critical Node Analysis. 11.3 Game Theory Considerations. 11.4 The General Attacker-Defender Network Risk Problem. 11.5 Critical Link Analysis. Table 11.4 Allocation of Resources via Flow Analysis. 11.6 Stability Resilience in Kirchhoff Networks. 11.6 Exercises. 12. NETGAIN. 12.1 Classical Diffusion Equations. 12.2 Multi-Product Networks. 12.3 Java Method for Netgain Emergence. 12.4 Nascent Market Networks. 12.5 Creative Destruction Networks. 12.6 Merger & Acquisition Networks. 12.7 Exercises. 13. BIOLOGY. 13.1 Static Models. 13.2 Dynamic Analysis. 13.3 Protein Expression Networks. 13.4 Mass Kinetics Modeling. 13.5 Exercises.
