
Small Worlds
Description
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The networks of this story are everywhere: the brain is a network of neurons; organisations are people networks; the global economy is a network of national economies, which are networks of markets, which are in turn networks of interacting producers and consumers. Food webs, ecosystems, and the Internet can all be represented as networks, as can strategies for solving a problem, topics in a conversation, and even words in a language. Many of these networks, the author claims, will turn out to be small worlds.
How do such networks matter? Simply put, local actions can have global consequences, and the relationship between local and global dynamics depends critically on the network's structure. Watts illustrates the subtleties of this relationship using a variety of simple models---the spread of infectious disease through a structured population; the evolution of cooperation in game theory; the computational capacity of cellular automata; and the sychronisation of coupled phase-oscillators.
Watts's novel approach is relevant to many problems that deal with network connectivity and complex systems' behaviour in general: How do diseases (or rumours) spread through social networks? How does cooperation evolve in large groups? How do cascading failures propagate through large power grids, or financial systems? What is the most efficient architecture for an organisation, or for a communications network? This fascinating exploration will be fruitful in a remarkable variety of fields, including physics and mathematics, as well as sociology, economics, and biology.
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Content
- Cover
- Title Page
- Copyright Page
- Contents
- PREFACE
- 1 Kevin Bacon, the Small World, and Why It All Matters
- PART I: STRUCTURE
- 2 An Overview of the Small-World Phenomenon
- 2.1 Social Networks and the Small World
- 2.1.1 A Brief History of the Small World
- 2.1.2 Difficulties with the Real World
- 2.1.3 Reframing the Question to Consider All Worlds
- 2.2 Background on the Theory of Graphs
- 2.2.1 Basic Definitions
- 2.2.2 Length and Length Scaling
- 2.2.3 Neighbourhoods and Distribution Sequences
- 2.2.4 Clustering
- 2.2.5 "Lattice Graphs" and Random Graphs
- 2.2.6 Dimension and Embedding of Graphs
- 2.2.7 Alternative Definition of Clustering Coefficient
- 3 Big Worlds and Small Worlds: Models of Graphs
- 3.1 Relational Graphs
- 3.1.1 a-Graphs
- 3.1.2 A Stripped-Down Model: ß-Graphs
- 3.1.3 Shortcuts and Contractions: Model Invariance
- 3.1.4 Lies, Damned Lies, and (More) Statistics
- 3.2 Spatial Graphs
- 3.2.1 Uniform Spatial Graphs
- 3.2.2 Gaussian Spatial Graphs
- 3.3 Main Points in Review
- 4 Explanations and Ruminations
- 4.1 Going to Extremes
- 4.1.1 The Connected-Caveman World
- 4.1.2 Moore Graphs as Approximate Random Graphs
- 4.2 Transitions in Relational Graphs
- 4.2.1 Local and Global Length Scales
- 4.2.2 Length and Length Scaling
- 4.2.3 Clustering Coefficient
- 4.2.4 Contractions
- 4.2.5 Results and Comparisons with ß-Model
- 4.3 Transitions in Spatial Graphs
- 4.3.1 Spatial Length versus Graph Length
- 4.3.2 Length and Length Scaling
- 4.3.3 Clustering
- 4.3.4 Results and Comparisons
- 4.4 Variations on Spatial and Relational Graphs
- 4.5 Main Points in Review
- 5 "It's a Small World after All": Three Real Graphs
- 5.1 Making Bacon
- 5.1.1 Examining the Graph
- 5.1.2 Comparisons
- 5.2 The Power of Networks
- 5.2.1 Examining the System
- 5.2.2 Comparisons
- 5.3 A Worm's Eye View
- 5.3.1 Examining the System
- 5.3.2 Comparisons
- 5.4 Other Systems
- 5.5 Main Points in Review
- PART II: DYNAMICS
- 6 The Spread of Infectious Disease in Structured Populations
- 6.1 A Brief Review of Disease Spreading
- 6.2 Analysis and Results
- 6.2.1 Introduction of the Problem
- 6.2.2 Permanent-Removal Dynamics
- 6.2.3 Temporary-Removal Dynamics
- 6.3 Main Points in Review
- 7 Global Computation in Cellular Automata
- 7.1 Background
- 7.1.1 Global Computation
- 7.2 Cellular Automata on Graphs
- 7.2.1 Density Classification
- 7.2.2 Synchronisation
- 7.3 Main Points in Review
- 8 Cooperation in a Small World: Games on Graphs
- 8.1 Background
- 8.1.1 The Prisoner's Dilemma
- 8.1.2 Spatial Prisoner's Dilemma
- 8.1.3 N-Player Prisoner's Dilemma
- 8.1.4 Evolution of Strategies
- 8.2 Emergence of Cooperation in a Homogeneous Population
- 8.2.1 Generalised Tit-for-Tat
- 8.2.2 Win-Stay, Lose-Shift
- 8.3 Evolution of Cooperation in a Heterogeneous Population
- 8.4 Main Points in Review
- 9 Global Synchrony in Populations of Coupled Phase Oscillators
- 9.1 Background
- 9.2 Kuramoto Oscillators on Graphs
- 9.3 Main Points in Review
- 10 Conclusions
- Notes
- Bibliography
- Index
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