A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks

Romy Blankendaal, Sarah Parinussa, Jan Treur

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

Abstract

This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model has been evaluated in three different manners: by simulation experiments, by verification based on mathematical analysis, and by validation against an empirical data set.
Original languageEnglish
Title of host publicationProc. ECAI'16
PublisherIOS Press
Pages1388-1396
Number of pages9
Volume285
ISBN (Electronic)978-1-61499-671-2
DOIs
Publication statusPublished - 2016
Event22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands
Duration: 29 Aug 20162 Sep 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Volume285
ISSN (Print)0922-6389

Conference

Conference22nd European Conference on Artificial Intelligence, ECAI 2016
CountryNetherlands
CityThe Hague
Period29/08/162/09/16

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  • Cite this

    Blankendaal, R., Parinussa, S., & Treur, J. (2016). A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks. In Proc. ECAI'16 (Vol. 285, pp. 1388-1396). (Frontiers in Artificial Intelligence and Applications; Vol. 285). IOS Press. https://doi.org/10.3233/978-1-61499-672-9-1388