Maximizing positive influence in competitive social networks: a trust-based solution
type: Item
instance of: scholarly article
title: Maximizing positive influence in competitive social networks: a trust-based solution
zbMATH Open document ID: 1479.91285
author: Feng Wang
author: Yasuhiro Ohyama
author: Wenjun Jiang
author: Geyong Min
author: Guojun Wang
author: Min Wu
author: Jinhua She
published in: Information Sciences
publication date: 2021-12-01T00:00:00Z
review text: This paper studies positive influence maximization in competitive social networks. A trust-based solution is proposed, which stresses on the trust relationship in online social networks. The model leverages on the competitive influence diffusion which models the dynamics of the influence spreads. Positive influence is modeled by trust and negative influence is modeled by distrust. To compute the influence probabilities in the trust-based competitive influence diffusion, the paper uses the generalized network flows methods. The paper also proposes a trust-based competitive influence maximization algorithm to derive the positive marginal gain for users who are not seeds. The results are tested on some realistic datasets and the experiments show good efficiency of the proposed method.
reviewed by: Yilun Shang
Mathematics Subject Classification ID: 91D30
zbMATH DE Number: 7436870
zbMATH Keywords: influence maximization
zbMATH Keywords: trust evaluation
zbMATH Keywords: competitive influence diffusion
zbMATH Keywords: online social networks
MaRDI profile type: Publication
full work available at URL: j.ins.2020.09.002
OpenAlex ID: W3084073868
cites work: Trust Evaluation in Online Social Networks Using Generalized Network Flow
cites work: A new algorithm for positive influence maximization in signed networks
cites work: Q2941641
cites work: Identification of influential users in social networks based on users' interest
DOI: 10.1016/J.INS.2020.09.002
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description: scientific article; zbMATH DE number 7436870