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

Sven van den Beukel; Simon Goos; J. Treur

doi:10.1007/978-3-319-61578-3_2

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 proceeding › Conference contribution › Academic › peer-review

9 Downloads (Pure)

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 language	English
Title of host publication	Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017
Editors	F De la Prieta
Publisher	Springer
Pages	16-29
Number of pages	14
ISBN (Electronic)	9783319615783
ISBN (Print)	9783319615776
DOIs	https://doi.org/10.1007/978-3-319-61578-3_2
Publication status	Published - 2017

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Number	619

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-61578-3_2

Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network ModelsFinal published version, 614 KBLicence: Article 25fa Dutch Copyright Act

https://www.researchgate.net/publication/315496675_Understanding_Homophily_and_More-Becomes-More_Through_Adaptive_Temporal-Causal_Network_Models

Persistent URL (handle)

Persistent link

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. https://doi.org/10.1007/978-3-319-61578-3_2

@inproceedings{ed775c1d257345778a87eab54c22090b,

title = "Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models",

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.",

author = "Beukel, {Sven van den} and Simon Goos and J. Treur",

year = "2017",

doi = "10.1007/978-3-319-61578-3_2",

language = "English",

isbn = "9783319615776",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

number = "619",

pages = "16--29",

editor = "{De la Prieta}, F",

booktitle = "Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017",

}

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. https://doi.org/10.1007/978-3-319-61578-3_2

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 proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Understanding Homophily and More-Becomes-More Through Adaptive Temporal-Causal Network Models

AU - Beukel, Sven van den

AU - Goos, Simon

AU - Treur, J.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-61578-3_2

DO - 10.1007/978-3-319-61578-3_2

M3 - Conference contribution

SN - 9783319615776

T3 - Advances in Intelligent Systems and Computing

SP - 16

EP - 29

BT - Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017

A2 - De la Prieta, F

PB - Springer

ER -

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). Epub 2017 Jul 13. doi: 10.1007/978-3-319-61578-3_2

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

Abstract

Publication series

UN SDGs

Access to Document

Persistent URL (handle)

Fingerprint

Cite this