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E-grāmata: Epidemics and Rumours in Complex Networks

(Imperial College of Science, Technology and Medicine, London),
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A concise introduction for applied mathematicians and computer scientists to modern approaches to epidemic modelling on networks.

Information propagation through peer-to-peer systems, online social systems, wireless mobile ad hoc networks and other modern structures can be modelled as an epidemic on a network of contacts. Understanding how epidemic processes interact with network topology allows us to predict ultimate course, understand phase transitions and develop strategies to control and optimise dissemination. This book is a concise introduction for applied mathematicians and computer scientists to basic models, analytical tools and mathematical and algorithmic results. Mathematical tools introduced include coupling methods, Poisson approximation (the Stein-Chen method), concentration inequalities (Chernoff bounds and Azuma-Hoeffding inequality) and branching processes. The authors examine the small-world phenomenon, preferential attachment, as well as classical epidemics. Each chapter ends with pointers to the wider literature. An ideal accompaniment for graduate courses, this book is also for researchers (statistical physicists, biologists, social scientists) who need an efficient guide to modern approaches to epidemic modelling on networks.

Recenzijas

'this is a nice introduction, at the level of a graduate course, to the propagation of biological epidemics and the spread of rumours in networks, aimed at students in computer science and applied probability.' Zentralblatt MATH

Papildus informācija

A concise introduction for applied mathematicians and computer scientists to modern approaches to epidemic modelling on networks.
Introduction 1(4)
PART I SHAPELESS NETWORKS
5(58)
Galton-Watson branching processes
7(12)
Introduction
7(2)
Depth-first exploration
9(1)
Breadth-first exploration
10(2)
One-by-one exploration
12(3)
Chernoff bounds and total population size
15(2)
Notes
17(1)
Problems
17(2)
Reed-Frost epidemics and Erdos-Renyi random graphs
19(16)
Introduction
19(2)
Emergence of the giant component
21(2)
The subcritical regime
23(2)
The supercritical regime
25(4)
The critical regime
29(4)
Notes
33(2)
Connectivity and Poisson approximation
35(11)
Introduction
35(1)
Variation distance
35(2)
The Stein-Chen method
37(3)
Emergence of connectivity in E-R graphs
40(4)
Notes
44(2)
Diameter of Erdos-Renyi graphs
46(8)
Introduction
46(2)
Diameter of E-R graphs
48(1)
Control of neighbourhood growth
49(2)
Bounds for the diameter
51(2)
Notes
53(1)
From microscopic to macroscopic dynamics
54(9)
Introduction
54(1)
Birth and death processes
55(2)
Kurtz's theorem
57(4)
Notes
61(2)
PART II STRUCTURED NETWORKS
63(54)
The small-world phenomenon
65(12)
Introduction
65(1)
Small world according to Strogatz and Watts
66(5)
Small world according to Kleinberg
71(5)
Notes
76(1)
Power laws via preferential attachment
77(10)
Introduction
77(1)
Barabasi-Albert random graphs
78(5)
Yule process
83(3)
Notes
86(1)
Epidemics on general graphs
87(21)
Introduction
87(1)
Reed-Frost model
88(2)
SIS model
90(10)
Epidemics on specific graphs
100(7)
Notes
107(1)
Viral marketing and optimised epidemics
108(9)
Model and motivation
108(1)
Algorithmic hardness
109(1)
Submodular structure and its consequences
110(2)
Viral marketing
112(3)
Notes
115(2)
References 117(5)
Index 122
Moez Draief is Assistant Professor in the Department of Electrical and Electronic Engineering at Imperial College, London. Laurent Massoulié is Senior Researcher at Thomson Corporate Research in Paris. He has been the recipient of several best paper awards including ACM CoNEXT 2007.