Department of Applied Mathematics, University of Colorado at Boulder, Boulder, Colorado 80309, USA.
Chaos. 2020 Oct;30(10):103117. doi: 10.1063/5.0020034.
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.
网络社交传播过程(如观点形成和流行病传播)的动态通常受到多个节点之间相互作用的调节。以前的研究结果表明,这些高阶相互作用可以深刻地改变传染病传播过程的动力学,导致双稳性、滞后和爆发性转变。在本文中,我们提出并分析了超图上易感-感染-易感模型的基于超度数的平均场描述,即具有高阶相互作用的网络,并通过一个例子说明了它的适用性,在这个例子中,传染病是通过链接(两两相互作用)和三角形(三向相互作用)来传播的。我们考虑了链接和三角形结构的各种组织模型以及不同的高阶传染病和治疗机制。我们发现,当链接和三角形是独立选择的,或者当链接和三角形的连接与不相关的情况相比呈正相关时,链接度分布的异质性可以抑制爆发性转变。我们通过传染病过程的微观模拟和从平均场模型得出的分析预测验证了这些结果。我们的结果表明,高阶相互作用的结构对超图上的传染病传播过程有重要影响。