Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models

Sven van den Beukel, Simon Goos, J. Treur

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-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.
Original languageEnglish
Title of host publicationTrends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017
EditorsF De la Prieta
PublisherSpringer
Pages16-29
Number of pages14
Publication statusPublished - 21 Jun 2017

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Number619

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Tobacco
Alcohols

Cite this

Beukel, S. V. D., Goos, S., & Treur, J. (2017). Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models. In F. De la Prieta (Ed.), Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017 (pp. 16-29). (Advances in Intelligent Systems and Computing; No. 619). Springer.
Beukel, Sven van den ; Goos, Simon ; Treur, J. / Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. editor / F De la Prieta. Springer, 2017. pp. 16-29 (Advances in Intelligent Systems and Computing; 619).
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Beukel, SVD, Goos, S & Treur, J 2017, Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models. in F De la Prieta (ed.), Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. Advances in Intelligent Systems and Computing, no. 619, Springer, pp. 16-29.

Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models. / Beukel, Sven van den; Goos, Simon; Treur, J.

Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. ed. / F De la Prieta. Springer, 2017. p. 16-29 (Advances in Intelligent Systems and Computing; No. 619).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Beukel SVD, Goos S, Treur J. Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models. In De la Prieta F, editor, Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. Springer. 2017. p. 16-29. (Advances in Intelligent Systems and Computing; 619).