Social contagion models on adaptive simplicial complexes

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Ziting Luo , Shuai Li , Zheng Jiang , Wei Chen
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引用次数: 0

Abstract

Complex networks, composed of nodes and edges connecting them, are successful in modeling various social contagion phenomena, such as epidemic spreading or rumor diffusion in population. Recently, there is extensive interest in studying higher-order interactions which uncover the complex mechanisms of influence and reinforcement in social contagion. Yet, existing models are primarily within the framework of static networks. Here we investigate the susceptible–infected–susceptible model on adaptive simplicial complexes, in which the networks exhibiting high-order structure can change their connectivity with time depending on their dynamical state. By applying mean-field equations, we derive an implicit analytical expression of the invasion threshold in adaptive simplicial complexes, but explicit expressions of the invasion threshold in three prototypical adaptive networks. We further analyze the effects of the transmission rate, the rewiring rate and higher-order interactions on epidemic prevalence and the invasion threshold. Our model can lead to collective phenomena including first-order phase transition, bistability, and hysteresis loops. Our study paves the way to predict and control a wide variety of social contagion processes on networks.
适应性简单复合体的社会传染模型
由节点和连接节点的边组成的复杂网络,成功地模拟了各种社会传染现象,如流行病传播或谣言在人群中的传播。近年来,研究高阶相互作用揭示了社会传染中影响和强化的复杂机制引起了人们的广泛兴趣。然而,现有的模型主要是在静态网络的框架内。本文研究了自适应简单复合体的易感-受感染-易感模型,该模型中具有高阶结构的网络可以根据其动态状态随时间改变其连通性。利用均场方程,导出了自适应简单复合体入侵阈值的隐式解析表达式,以及三个典型自适应网络入侵阈值的显式表达式。我们进一步分析了传播速率、重布线速率和高阶相互作用对流行率和入侵阈值的影响。我们的模型可以导致集体现象,包括一阶相变、双稳态和滞后回路。我们的研究为预测和控制网络上各种各样的社会传染过程铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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