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Journal of Social Computing 2021, 2(2): 166-182 https://doi.org/10.23919/JSC.2021.0007
Open Access | Issue | Published: 23 August 2021

An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Show Author's Information Chenting Jiang1,  Anthony D’Arienzo2,  Weihua Li3( ),  Shiqing Wu1,  Quan Bai1( )
School of Information and Communication Technology, University of Tasmania, Hobart 7005, Australia
Department of Mathematics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Keywords:
influence diffusion, influence maximization, complex social networks, operator-based model, heat diffusion-based influence modeling
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Jiang C, D’Arienzo A, Li W, et al. An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks. Journal of Social Computing, 2021, 2(2): 166-182. https://doi.org/10.23919/JSC.2021.0007
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Abstract Full text About this article

Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.


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An Operator-Based Approach for Modeling Influence Diffusion in Complex Social Networks

Show Author's information Chenting Jiang1, Anthony D’Arienzo2, Weihua Li3( ), Shiqing Wu1, Quan Bai1( )
School of Information and Communication Technology, University of Tasmania, Hobart 7005, Australia
Department of Mathematics, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand

Abstract

Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.

Keywords: influence diffusion, influence maximization, complex social networks, operator-based model, heat diffusion-based influence modeling

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Publication history
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Publication history

Received: 31 May 2021
Revised: 14 June 2021
Accepted: 22 June 2021
Published: 23 August 2021
Issue date: June 2021

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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