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Diffusion in Social Networks

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  • © 2015

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Authors:
  1. Paulo Shakarian

    1. School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, USA

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  2. Abhivav Bhatnagar

    1. Arizona State University, Tempe, USA

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  3. Ashkan Aleali

    1. Arizona State University, Tempe, USA

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  4. Elham Shaabani

    1. Arizona State University, Tempe, USA

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  5. Ruocheng Guo

    1. Arizona State University, Tempe, USA

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Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Introduction

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 1-2

  3. The SIR Model and Identification of Spreaders

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 3-18

  4. The Tipping Model and the Minimum Seed Problem

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 19-33

  5. The Independent Cascade and Linear Threshold Models

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 35-48

  6. Logic Programming Based Diffusion Models

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 49-73

  7. Evolutionary Graph Theory

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 75-91

  8. Examining Diffusion in the Real World

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 93-100

  9. Conclusion

    • Paulo Shakarian, Abhinav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo
    Pages 101-101
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Keywords

About this book

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.

Authors and Affiliations

  • School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, USA

    Paulo Shakarian

  • Arizona State University, Tempe, USA

    Abhivav Bhatnagar, Ashkan Aleali, Elham Shaabani, Ruocheng Guo

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