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A Survey of Models and Algorithms for Social Influence Analysis

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Social Network Data Analytics

Abstract

Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this chapter, we survey the research on social influence analysis with a focus on the computational aspects. First, we present statistical measurements related to social influence. Second, we describe the literature on social similarity and influences. Third, we present the research on social influence maximization which has many practical applications including marketing and advertisement.

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Authors and Affiliations

  1. IBM TJ Watson Research Center, Hawthorne, NY, USA

    Jimeng Sun

  2. Tsinghua University, Beijing, China

    Jie Tang

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  1. Jimeng Sun
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  2. Jie Tang
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Correspondence to Jimeng Sun .

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  1. Thomas J. Watson Research Center, IBM, Skyline Drive 19, Hawthorne, 10532, New York, USA

    Charu C. Aggarwal

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Sun, J., Tang, J. (2011). A Survey of Models and Algorithms for Social Influence Analysis. In: Aggarwal, C. (eds) Social Network Data Analytics. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8462-3_7

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  • DOI: https://doi.org/10.1007/978-1-4419-8462-3_7

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