Diffusion in Social Networks

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Networking & Communications, Computer Security, General Computing
Cover of the book Diffusion in Social Networks by Ashkan Aleali, Paulo Shakarian, Abhivav Bhatnagar, Ruocheng Guo, Elham Shaabani, Springer International Publishing
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Author: Ashkan Aleali, Paulo Shakarian, Abhivav Bhatnagar, Ruocheng Guo, Elham Shaabani ISBN: 9783319231051
Publisher: Springer International Publishing Publication: September 16, 2015
Imprint: Springer Language: English
Author: Ashkan Aleali, Paulo Shakarian, Abhivav Bhatnagar, Ruocheng Guo, Elham Shaabani
ISBN: 9783319231051
Publisher: Springer International Publishing
Publication: September 16, 2015
Imprint: Springer
Language: English

This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.

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This book presents the leading models of social network diffusion that are used to demonstrate the spread of disease, ideas, and behavior. It introduces diffusion models from the fields of computer science (independent cascade and linear threshold), sociology (tipping models), physics (voter models), biology (evolutionary models), and epidemiology (SIR/SIS and related models). A variety of properties and problems related to these models are discussed including identifying seeds sets to initiate diffusion, game theoretic problems, predicting diffusion events, and more. The book explores numerous connections between social network diffusion research and artificial intelligence through topics such as agent-based modeling, logic programming, game theory, learning, and data mining. The book also surveys key empirical results in social network diffusion, and reviews the classic and cutting-edge research with a focus on open problems.

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