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The effect of heterogeneity on hypergraph contagion models.
Landry, Nicholas W; Restrepo, Juan G.
Afiliação
  • Landry NW; Department of Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado 80309, USA.
  • Restrepo JG; Department of Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado 80309, USA.
Chaos ; 30(10): 103117, 2020 Oct.
Article em En | MEDLINE | ID: mdl-33138447
The dynamics of network social contagion processes such as opinion formation and epidemic spreading are often mediated by interactions between multiple nodes. Previous results have shown that these higher-order interactions can profoundly modify the dynamics of contagion processes, resulting in bistability, hysteresis, and explosive transitions. In this paper, we present and analyze a hyperdegree-based mean-field description of the dynamics of the susceptible-infected-susceptible model on hypergraphs, i.e., networks with higher-order interactions, and illustrate its applicability with the example of a hypergraph where contagion is mediated by both links (pairwise interactions) and triangles (three-way interactions). We consider various models for the organization of link and triangle structures and different mechanisms of higher-order contagion and healing. We find that explosive transitions can be suppressed by heterogeneity in the link degree distribution when links and triangles are chosen independently or when link and triangle connections are positively correlated when compared to the uncorrelated case. We verify these results with microscopic simulations of the contagion process and with analytic predictions derived from the mean-field model. Our results show that the structure of higher-order interactions can have important effects on contagion processes on hypergraphs.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epidemias / Rede Social / Processos Grupais / Infecções / Comportamento de Massa / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Chaos Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Epidemias / Rede Social / Processos Grupais / Infecções / Comportamento de Massa / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Chaos Ano de publicação: 2020 Tipo de documento: Article