Modelling and Analysis of Social Contagion in Dynamic Networks

A. Sharpanskykh, J. Treur

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analyzed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model - the ability to form clusters - is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified. Moreover, model abstraction methods are proposed by using which aggregated opinion states of clusters of agents can be approximated with a high accuracy. These techniques also improve the computational efficiency of social contagion models (up to 6 times). © 2014 Elsevier B.V.
Original languageEnglish
Pages (from-to)140-150
JournalNeurocomputing
Volume146
DOIs
Publication statusPublished - 2014

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Complex networks
Computational efficiency

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title = "Modelling and Analysis of Social Contagion in Dynamic Networks",
abstract = "In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analyzed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model - the ability to form clusters - is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified. Moreover, model abstraction methods are proposed by using which aggregated opinion states of clusters of agents can be approximated with a high accuracy. These techniques also improve the computational efficiency of social contagion models (up to 6 times). {\circledC} 2014 Elsevier B.V.",
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Modelling and Analysis of Social Contagion in Dynamic Networks. / Sharpanskykh, A.; Treur, J.

In: Neurocomputing, Vol. 146, 2014, p. 140-150.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Modelling and Analysis of Social Contagion in Dynamic Networks

AU - Sharpanskykh, A.

AU - Treur, J.

PY - 2014

Y1 - 2014

N2 - In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analyzed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model - the ability to form clusters - is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified. Moreover, model abstraction methods are proposed by using which aggregated opinion states of clusters of agents can be approximated with a high accuracy. These techniques also improve the computational efficiency of social contagion models (up to 6 times). © 2014 Elsevier B.V.

AB - In this paper an agent-based social contagion model with an underlying dynamic network is proposed and analyzed. In contrast to the existing social contagion models, the strength of links between agents changes gradually rather than abruptly based on a threshold mechanism. An essential feature of the model - the ability to form clusters - is extensively investigated in the paper analytically and by simulation. Specifically, the distribution of clusters in random and scale-free networks is investigated, the dynamics of links within and between clusters are determined, the minimal distance between two clusters is identified. Moreover, model abstraction methods are proposed by using which aggregated opinion states of clusters of agents can be approximated with a high accuracy. These techniques also improve the computational efficiency of social contagion models (up to 6 times). © 2014 Elsevier B.V.

U2 - 10.1016/j.neucom.2014.03.069

DO - 10.1016/j.neucom.2014.03.069

M3 - Article

VL - 146

SP - 140

EP - 150

JO - Neurocomputing

JF - Neurocomputing

SN - 0925-2312

ER -