Small Worlds: The Dynamics of Networks between Order and Randomness

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Overview

Everyone knows the small-world phenomenon: soon after meeting a stranger, we are surprised to discover that we have a mutual friend, or we are connected through a short chain of acquaintances. Duncan Watts uses this intriguing phenomenon -- colloquially called "six degrees of separation" -- as a prelude to a more general exploration: under what conditions can a small world arise in any kind of network? The networks of this story are everywhere: the brain is a network of neurons; organizations 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. 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 fascinating models. This exploration will be valuable to many fields, including physics and mathematics, as well as sociology, economics, and biology.
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Editorial Reviews

Quarterly Review of Biology - Peter Kareiva
Playfully and clearly written. . . . [Watts] uses examples adroitly, and mixes abstract theory with real-world anecdotes with superb skill. . . . I have not enjoyed reading a book this much in a long time.
New Scientist - Robert Matthews
[Small Worlds] will be seized on by those seeking a first rough map of this fascinating new mathematical land. Those entering can expect to find some amazing connections between areas of research with apparently nothing in common, such as neurology to business studies. But then, it's a small world.
From the Publisher
"An engaging and informative introduction."—Science

"Playfully and clearly written. . . . [Watts] uses examples adroitly, and mixes abstract theory with real-world anecdotes with superb skill. . . . I have not enjoyed reading a book this much in a long time."—Peter Kareiva, Quarterly Review of Biology

"[Small Worlds] will be seized on by those seeking a first rough map of this fascinating new mathematical land. Those entering can expect to find some amazing connections between areas of research with apparently nothing in common, such as neurology to business studies. But then, it's a small world."—Robert Matthews, New Scientist

"Informally written and aimed at a wide audience, this book shows how mathematics yields new vistas on ubiquitous and seemingly familiar aspects of our world."—Choice

Science
An engaging and informative introduction.
Quarterly Review of Biology
Playfully and clearly written. . . . [Watts] uses examples adroitly, and mixes abstract theory with real-world anecdotes with superb skill. . . . I have not enjoyed reading a book this much in a long time.
— Peter Kareiva
New Scientist
[Small Worlds] will be seized on by those seeking a first rough map of this fascinating new mathematical land. Those entering can expect to find some amazing connections between areas of research with apparently nothing in common, such as neurology to business studies. But then, it's a small world.
— Robert Matthews
Choice
Informally written and aimed at a wide audience, this book shows how mathematics yields new vistas on ubiquitous and seemingly familiar aspects of our world.
Read More Show Less

Product Details

  • ISBN-13: 9780691117041
  • Publisher: Princeton University Press
  • Publication date: 11/24/2003
  • Series: Princeton Studies in Complexity Series
  • Pages: 264
  • Sales rank: 1,049,739
  • Product dimensions: 5.88 (w) x 9.42 (h) x 0.41 (d)

Table of Contents

PREFACE xiii
1 Kevin Bacon, the Small World, and Why It All Matters 3
PART I STRUCTURE 9
2 An Overview of the Small-World Phenomenon 11
2.1 Social Networks and the Small World 11
2.1.1 A Brief History of the Small World 12
2.1.2 Difficulties with the Real World 20
2.1.3 Reframing the Question to Consider All Worlds 24
2.2 Background on the Theory of Graphs 25
2.2.1 Basic Definitions 25
2.2.2 Length and Length Scaling 27
2.2.3 Neighbourhoods and Distribution Sequences 31
2.2.4 Clustering 32
2.2.5 "Lattice Graphs" and Random Graphs 33
2.2.6 Dimension and Embedding of Graphs 39
3 Big Worlds and Small Worlds: Models of Graphs 41
3.1 Relational Graphs 42
3.1.1 a-Graphs 42
3.1.2 A Stripped-Down Model: B-Graphs 66
3.1.3 Shortcuts and Contractions: Model Invariance 70
3.1.4 Lies, Damned Lies, and (More) Statistics 87
3.2 Spatial Graphs 91
3.2.1 Uniform Spatial Graphs 93
3.2.2 Gaussian Spatial Graphs 98
3.3 Main Points in Review 100
4 Explanations and Ruminations 101
4.1 Going to Extremes 101
4.1.1 The Connected-Caveman World 102
4.1.2 Moore Graphs as Approximate Random Graphs 109
4.2 Transitions in Relational Graphs 114
4.2.1 Local and Global Length Scales 114
4.2.2 Length and Length Scaling 116
4.2.3 Clustering Coefficient 117
4.2.4 Contractions 118
4.2.5 Results and Comparisons with B-Model 120
4.3 Transitions in Spatial Graphs 127
4.3.1 Spatial Length versus Graph Length 127
4.3.2 Length and Length Scaling 128
4.3.3 Clustering 130
4.3.4 Results and Comparisons 132
4.4 Variations on Spatial and Relational Graphs 133
4.5 Main Points in Review 136
5 "It's a Small World after All": Three Real Graphs 138
5.1 Making Bacon 140
5.1.1 Examining the Graph 141
5.1.2 Comparisons 143
5.2 The Power of Networks 147
5.2.1 Examining the System 147
5.2.2 Comparisons 150
5.3 A Worm's Eye View 153
5.3.1 Examining the System 154
5.3.2 Comparisons 156
5.4 Other Systems 159
5.5 Main Points in Review 161
PART II DYNAMICS 163
6 The Spread of Infectious Disease in Structured Populations 165
6.1 A Brief Review of Disease Spreading 166
6.2 Analysis and Results 168
6.2.1 Introduction of the Problem 168
6.2.2 Permanent-Removal Dynamics 169
6.2.3 Temporary-Removal Dynamics 176
6.3 Main Points in Review 180
7 Global Computation in Cellular Automata 181
7.1 Background 181
7.1.1 Global Computation 184
7.2 Cellular Automata on Graphs 187
7.2.1 Density Classification 187
7.2.2 Synchronisation 195
7.3 Main Points in Review 198
8 Cooperation in a Small World: Games on Graphs 199
8.1 Background 199
8.1.1 The Prisoner's Dilemma 200
8.1.2 Spatial Prisoner's Dilemma 204
8.1.3 N-Player Prisoner's Dilemma 206
8.1.4 Evolution of Strategies 207
8.2 Emergence of Cooperation in a Homogeneous Population 208
8.2.1 Generalised Tit-for-Tat 209
8.2.2 Win-Stay, Lose-Shift 214
8.3 Evolution of Cooperation in a Heterogeneous Population 219
8.4 Main Points in Review 221
9 Global Synchrony in Populations of Coupled Phase Oscillators 223
9.1 Background 223
9.2 Kuramoto Oscillators on Graphs 228
9.3 Main Points in Review 238
10 Conclusions 240
NOTES 243
BIBLIOGRAPHY 249
INDEX 257

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