大葉大學圖書館 |

Diffusion in social networks[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.233
書名/作者:	Diffusion in social networks/ by Paulo Shakarian ... [et al.].
其他作者:	Shakarian, Paulo.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xi, 101 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Diffusion processes.
標題:	Social networks - Mathematical models.
標題:	Social networks - Computer simulation.
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Data Encryption.
ISBN:	9783319231051
ISBN:	9783319231044
內容註:	Introduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23105-1

Diffusion in social networks[electronic resource] /
Diffusion in social networks[electronic resource] /by Paulo Shakarian ... [et al.]. - Cham :Springer International Publishing :2015. - xi, 101 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

Introduction -- The SIR Model and Identification of Spreaders -- The Tipping Model and the Minimum Seed Problem -- The Independent Cascade and Linear Threshold Models -- Logic Programming Based Diffusion Models -- Evolutionary Graph Theory -- Examining Diffusion in the Real World -- Conclusion.

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.

ISBN: 9783319231051

Standard No.: 10.1007/978-3-319-23105-1doiSubjects--Topical Terms:

418246
Diffusion processes.

LC Class. No.: QA274.25

Dewey Class. No.: 519.233

Diffusion in social networks[electronic resource] /
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520 $a 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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