Insights from exact social contagion dynamics on networks with higher-order structures

IF 2.2 4区 数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
István Kiss, Iacopo Iacopini, P'eter L. Simon, N. Georgiou
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引用次数: 0

Abstract

Recently, there has been an increasing interest in studying dynamical processes on networks exhibiting higher-order structures, such as simplicial complexes, where the dynamics acts above and beyond dyadic interactions. Using simulations or heuristically derived epidemic spreading models, it was shown that new phenomena can emerge, such as bi-stability/multistability. Here, we show that such new emerging phenomena do not require complex contact patterns, such as community structures, but naturally result from the higher-order contagion mechanisms. We show this by deriving an exact higher-order Susceptible-Infected-Susceptible model and its limiting mean-field equivalent for fully connected simplicial complexes. Going beyond previous results, we also give the global bifurcation picture for networks with 3- and 4-body interactions, with the latter allowing for two non-trivial stable endemic steady states. Differently from previous approaches, we are able to study systems featuring interactions of arbitrary order. In addition, we characterize the contributions from higher-order infections to the endemic equilibrium as perturbations of the pairwise baseline, finding that these diminish as the pairwise rate of infection increases. Our approach represents a first step towards a principled understanding of higher-order contagion processes beyond triads and opens up further directions for analytical investigations.
从具有高阶结构网络的精确社会传染动力学中获得启示
近来,人们对研究具有高阶结构(如简单复合物)的网络上的动力学过程越来越感兴趣,在这种网络上,动力学作用超越了二元相互作用。利用模拟或启发式推导的流行病传播模型,研究表明会出现新的现象,例如双稳态/多态性。在这里,我们证明了这种新出现的现象并不需要复杂的接触模式(如群落结构),而是由高阶传染机制自然产生的。为了证明这一点,我们推导出了一个精确的高阶 "易感-感染-易感 "模型,以及它在全连接简单复合物中的极限均场等效模型。在以往成果的基础上,我们还给出了具有三体和四体相互作用的网络的全局分岔图,其中四体相互作用允许出现两种非三体稳定的流行稳态。与以往的方法不同,我们能够研究任意阶的相互作用系统。此外,我们将高阶感染对流行平衡的贡献描述为对基线的扰动,发现这些贡献会随着对感染率的增加而减小。我们的方法代表了对三元组以外的高阶传染过程的原则性理解的第一步,并为分析研究开辟了进一步的方向。
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来源期刊
Journal of complex networks
Journal of complex networks MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.20
自引率
9.50%
发文量
40
期刊介绍: Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network
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