Dynamical analysis of a stochastic Hyper-INPR competitive information propagation model

Hypergraphs can capture higher-order interactions in communication networks, which surpasses simple one-to-one connections in ordinary graphs. In this paper, a stochastic hyper ignorant-negative-positive-remover (Hyper-INPR) competitive information propagation model is proposed for group diffusion b...

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Bibliographic Details
Published inChaos, solitons and fractals Vol. 185; p. 115073
Main Authors Xia, Yang, Jiang, Haijun, Mei, Xuehui, Li, Jiarong, Yu, Shuzhen
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.08.2024
Subjects
Competitive information
Hypergraph
Partial rank correlation coefficient (PRCC)
Stochastic Hyper-INPR
Stochastic stability theory
Hypergraph
Competitive information
Stochastic stability theory
Partial rank correlation coefficient (PRCC)
Stochastic Hyper-INPR
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Summary:Hypergraphs can capture higher-order interactions in communication networks, which surpasses simple one-to-one connections in ordinary graphs. In this paper, a stochastic hyper ignorant-negative-positive-remover (Hyper-INPR) competitive information propagation model is proposed for group diffusion based on random hypergraphs. Meanwhile, this model uses hyperpaths to depict the information propagation process. Furthermore, some conditions that judge the disappearance or prevalence of competitive information are acquired via stochastic stability theory. Especially, the partial rank correlation coefficient (PRCC) shows that the influence of hypergraphs on model parameters is different from those of ordinary graphs. Finally, some numerical simulations verify the plausibility of the results, and a real case shows the applicability of the model.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2024.115073