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

Romy Blankendaal; Sarah Parinussa; Jan Treur

doi:10.3233/978-1-61499-672-9-1388

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 proceeding › Conference contribution › Academic › peer-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 language	English
Title of host publication	Proc. ECAI'16
Publisher	IOS Press
Pages	1388-1396
Number of pages	9
Volume	285
ISBN (Electronic)	978-1-61499-671-2
DOIs	https://doi.org/10.3233/978-1-61499-672-9-1388
Publication status	Published - 2016
Event	22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands Duration: 29 Aug 2016 → 2 Sept 2016

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Publisher	IOS Press
Volume	285
ISSN (Print)	0922-6389

Conference

Conference	22nd European Conference on Artificial Intelligence, ECAI 2016
Country/Territory	Netherlands
City	The Hague
Period	29/08/16 → 2/09/16

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10.3233/978-1-61499-672-9-1388

https://www.researchgate.net/publication/303996475_A_Temporal-Causal_Modelling_Approach_to_Integrated_Contagion_and_Network_Change_in_Social_Networks

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title = "A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks",

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.",

author = "Romy Blankendaal and Sarah Parinussa and Jan Treur",

year = "2016",

doi = "10.3233/978-1-61499-672-9-1388",

language = "English",

volume = "285",

series = "Frontiers in Artificial Intelligence and Applications",

publisher = "IOS Press",

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booktitle = "Proc. ECAI'16",

note = "22nd European Conference on Artificial Intelligence, ECAI 2016 ; Conference date: 29-08-2016 Through 02-09-2016",

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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, Frontiers in Artificial Intelligence and Applications, vol. 285, IOS Press, pp. 1388-1396, 22nd European Conference on Artificial Intelligence, ECAI 2016, The Hague, Netherlands, 29/08/16. https://doi.org/10.3233/978-1-61499-672-9-1388

A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks. / Blankendaal, Romy; Parinussa, Sarah; Treur, Jan.
Proc. ECAI'16. Vol. 285 IOS Press, 2016. p. 1388-1396 (Frontiers in Artificial Intelligence and Applications; Vol. 285).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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

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