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Epidemics and rumours in complex 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,...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Draief, Moez, 1978-
Otros Autores: Massoulié, Laurent
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge ; New York : Cambridge University Press, 2010.
Colección:London Mathematical Society lecture note series ; 369.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Epidemics and rumours in complex networks /  |c Moez Draief, Laurent Massoulié. 
246 3 |a Epidemics and rumors in complex networks 
260 |a Cambridge ;  |a New York :  |b Cambridge University Press,  |c 2010. 
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490 1 |a London Mathematical Society lecture note series ;  |v 369 
504 |a Includes bibliographical references and index. 
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505 0 0 |t Galton-Watson branching processes --  |t Reed-Frost epidemics and Erdős-Rényi random graphs --  |t Connectivity and Poisson approximation --  |t Diameter of Erdős-Rényi graphs --  |t From microscopic to macroscopic dynamics --  |t The small-world phenomenon --  |t Power laws via preferential attachment --  |t Epidemics on general graphs --  |t Viral marketing and optimised epidemics. 
520 |a 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. 
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