An adaptive temporal-causal network model for social networks based on the homophily and more-becomes-more principle

Sven van den Beukel, Simon H. Goos, Jan Treur

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This study describes the use of adaptive temporal-causal networks to model and simulate the development of mutually interacting opinion states and connections between individuals in social networks. The focus is on adaptive networks combining the homophily principle with the more becomes more principle. The model has been used to analyse a data set concerning opinions about the use of alcohol and tobacco, and friendship relations. The achieved results provide insights in the potential of the approach.

LanguageEnglish
Pages361-371
Number of pages11
JournalNeurocomputing
Volume338
Early online date4 Feb 2019
DOIs
Publication statusE-pub ahead of print - 4 Feb 2019

Fingerprint

Tobacco Use
Social Support
Alcohols
Tobacco
Datasets

Keywords

  • Alcohol
  • Homophily
  • More becomes more
  • Temporal-causal networks
  • Tobacco

Cite this

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An adaptive temporal-causal network model for social networks based on the homophily and more-becomes-more principle. / Beukel, Sven van den; Goos, Simon H.; Treur, Jan.

In: Neurocomputing, Vol. 338, 04.02.2019, p. 361-371.

Research output: Contribution to JournalArticleAcademicpeer-review

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