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

Yang Xia, Haijun Jiang, Xuehui Mei, Jiarong Li and Shuzhen Yu

Chaos, Solitons & Fractals, 2024, vol. 185, issue C

Abstract: 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.

Keywords: Stochastic Hyper-INPR; Competitive information; Hypergraph; Stochastic stability theory; Partial rank correlation coefficient (PRCC) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:185:y:2024:i:c:s0960077924006258

DOI: 10.1016/j.chaos.2024.115073

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