Diffusion in Social Networks
Published on 28. September 2015
Book
Paperback/Softback
XI, 101 pages
978-3-319-23104-4 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2015
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
24 farbige Abbildungen, 4 s/w Abbildungen
XI, 101 p. 28 illus., 24 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 7 mm
Weight
211 gr
ISBN-13
978-3-319-23104-4 (9783319231044)
DOI
10.1007/978-3-319-23105-1
Schweitzer Classification
Other editions
Additional editions
Paulo Shakarian | Abhivav Bhatnagar | Ashkan Aleali
Diffusion in Social Networks
E-Book
09/2015
Springer
€53.49
Available for download
Persons
Paulo Shakarian is an associate professor at Arizona State University. His research focuses on symbolic AI and hybrid symbolic-ML systems. He received his Ph.D. from the University of Maryland, College Park. He is a past DARPA Military Fellow, AFOSR Young Investigator recipient, and his work earned multiple "best paper" awards.
Gerardo I. Simari is a professor at UNS, and a researcher at CONICET. His research focuses on AI and Databases, and reasoning under uncertainty. He received a PhD in computer science from University of Maryland College Park and later joined the Department of Computer Science, University of Oxford, where he was also a Fulford Junior Research Fellow of Somerville College.
Chitta Baral is a Professor at the Arizona State University, and a past President of KR Inc. His research interests include Knowledge Representation and Reasoning, NLP and Image Understanding and often involves combining logical reasoning with explicit knowledge and neural learning and reasoning with textual and perceptual inputs.
Bowen Xi is a Ph.D. student at Arizona State University, specializing in the field of Neural Symbolic AI. She is passionate about combining the strengths of neural networks and symbolic reasoning to advance the field of artificial intelligence. Bowen's research interests include developing novel algorithms and techniques that enable machines to learn and reason like humans.
Lahari Pokala is a student pursuing her Master's degree at Arizona State University, where she is majoring in Computer Science. Her interests lie in artificial intelligence and data engineering.
Content
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.