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International Journal of Crowd Science 2019, 3(2): 168-183 https://doi.org/10.1108/IJCS-01-2019-0005
Research paper | Open Access | Issue | Published: 13 August 2019

An epidemic model for correlated information diffusion in crowd intelligence networks

Show Author's Information Yuejiang Li1( ),  H. Vicky Zhao1,  Yan Chen2
Department of Automation and Institute for Artificial Intelligence, Tsinghua University, Beijing National Research Center for Information Science and Technology BNRist, Beijing, China
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Keywords:
Information diffusion, Crowd intelligence, SIR model
Cite this article:
Li Y, Zhao HV, Chen Y. An epidemic model for correlated information diffusion in crowd intelligence networks. International Journal of Crowd Science, 2019, 3(2): 168-183. https://doi.org/10.1108/IJCS-01-2019-0005
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Abstract Full text About this article
Purpose

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

Design/methodology/approach

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

Findings

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

Originality/value

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.


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About this article

An epidemic model for correlated information diffusion in crowd intelligence networks

Show Author's information Yuejiang Li1( ), H. Vicky Zhao1, Yan Chen2
Department of Automation and Institute for Artificial Intelligence, Tsinghua University, Beijing National Research Center for Information Science and Technology BNRist, Beijing, China
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract

Purpose

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

Design/methodology/approach

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

Findings

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

Originality/value

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

Keywords: Information diffusion, Crowd intelligence, SIR model

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Publication history
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Acknowledgements
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Publication history

Received: 30 January 2019
Revised: 25 May 2019
Accepted: 27 May 2019
Published: 13 August 2019
Issue date: September 2019

Copyright

© The author(s)

Acknowledgements

Acknowledgements

This work is supported by the National Key Research and Development Program of China (2017YFB1400100).

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Yuejiang Li, H. Vicky Zhao and Yan Chen. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

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