Generating artificial social networks

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The study of complex social networks is an inherently interdisciplinary research area with applications across many fields, including psychology. Social network models describe, illustrate and explain how people are connected to each other and can, for example, be used to study information spread and interconnectedness of people with different kinds of traits. One approach to social network modelling, originating mainly in the physics literature, is to generate targeted kinds of social networks using models with specialized mechanisms while analyzing and deriving features of the models. Surprisingly though, and despite the popularity of this approach, there is no available functionality for generating a wide variety of social networks from these models. Thus, researchers are left to implement and specify these models themselves, restricting the applicability of these models. In this article, I provide a set of Matlab functions enabling the generation of artificial social networks from 22 different network models, most of them explicitly designed to capture features of social networks. Many of these models originate in the physics literature and may therefore not be familiar to psychological researchers. I also provide an illustration of how these models can be evaluated in terms of a simulated model comparison approach and how they can be applied to psychological research. With the already existing network functionality available in Matlab and other languages, this should provide a useful extension to researchers.

Original languageEnglish
Pages (from-to)56-74
Number of pages18
JournalThe Quantitative Methods for Psychology
Issue number2
Publication statusPublished - 2019

Swedish Standard Keywords

  • Psychology (50101)


  • Clustering
  • Community.
  • Function
  • Matlab
  • Model
  • Psychology
  • Social Network


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