Network analysis for active and passive propagation models

In Jae Kim, Brian P. Barthel, Yuyoung Park, Jordan R. Tait, Joseph L. Dobmeier, Sung Kim, Dooyoung Shin

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


A large number of studies have been conducted over a long period of time to better understand how people adopt new products and/or ideas. With recent advancement in social network technologies such as Facebook and Twitter, the interest in better understanding the role of social network effect or social influence on the adoption of an idea has again been brought to the forefront. In this study, we examine the propagation of ideas through a social network and we introduce two network propagation rules to explain the idea propagation under social influence. We then apply the rules to different network models, which include a large-world network, a small-world network, a random network, and local neighbor networks such as a path, a star, and their variations. In terms of social network in our study, we say that if a person has adopted an idea, then the person is infected by the idea. So the main interest of our study is to find the minimum number of initially infected individuals (i.e., early adopters of an idea) to effectively infect the rest of the social network. Furthermore, we give examples of real-world situations in management and the spread of violence, where the proposed propagation analysis can be applied. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(2), 160-169 2014

Original languageEnglish (US)
Pages (from-to)160-169
Number of pages10
Issue number2
StatePublished - Mar 2014
Externally publishedYes


  • NP-hard
  • independence number
  • neighbor forcing number
  • network
  • propagation model
  • small-world
  • vertex cover number
  • zero forcing number


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