当前位置: X-MOL 学术J. Phys. Complex › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Simplicial contagion in temporal higher-order networks
Journal of Physics: Complexity Pub Date : 2021-07-27 , DOI: 10.1088/2632-072x/ac12bd
Sandeep Chowdhary 1 , Aanjaneya Kumar 2 , Giulia Cencetti 3 , Iacopo Iacopini 1, 4 , Federico Battiston 1
Affiliation  

Complex networks represent the natural backbone to study epidemic processes in populations of interacting individuals. Such a modeling framework, however, is naturally limited to pairwise interactions, making it less suitable to properly describe social contagion, where individuals acquire new norms or ideas after simultaneous exposure to multiple sources of infections. Simplicial contagion has been proposed as an alternative framework where simplices are used to encode group interactions of any order. The presence of these higher-order interactions leads to explosive epidemic transitions and bistability. In particular, critical mass effects can emerge even for infectivity values below the standard pairwise epidemic threshold, where the size of the initial seed of infectious nodes determines whether the system would eventually fall in the endemic or the healthy state. Here we extend simplicial contagion to time-varying networks, where pairwise and higher-order simplices can be created or destroyed over time. By following a microscopic Markov chain approach, we find that the same seed of infectious nodes might or might not lead to an endemic stationary state, depending on the temporal properties of the underlying network structure, and show that persistent temporal interactions anticipate the onset of the endemic state in finite-size systems. We characterize this behavior on higher-order networks with a prescribed temporal correlation between consecutive interactions and on heterogeneous simplicial complexes, showing that temporality again limits the effect of higher-order spreading, but in a less pronounced way than for homogeneous structures. Our work suggests the importance of incorporating temporality, a realistic feature of many real-world systems, into the investigation of dynamical processes beyond pairwise interactions.



中文翻译:

时间高阶网络中的简单传染

复杂网络代表了研究相互作用个体群体中流行病过程的自然支柱。然而,这样的建模框架自然仅限于成对交互,因此不太适合正确描述社会传染,即个人在同时暴露于多种感染源后获得新规范或想法。单纯传染已被提议作为一种替代框架,其中单纯被用于编码任何顺序的群体交互。这些高阶相互作用的存在导致爆发性的流行转变和双稳态。特别是,即使传染性值低于标准的成对流行阈值,也会出现临界质量效应,其中感染节点的初始种子大小决定了系统最终会处于流行状态还是健康状态。在这里,我们将单纯传染扩展到时变网络,其中可以随着时间的推移创建或销毁成对和高阶单纯形。通过遵循微观马尔可夫链方法,我们发现传染性节点的相同种子可能会或可能不会导致地方性静止状态,这取决于底层网络结构的时间特性,并表明持续的时间相互作用预计会发生有限大小系统中的流行状态。我们在连续交互和异构单纯复形之间具有规定的时间相关性的高阶网络上描述了这种行为,表明时间性再次限制了高阶传播的影响,但不如同质结构那么明显。我们的工作表明,将时间性(许多现实世界系统的现实特征)纳入对成对相互作用之外的动态过程的研究中的重要性。

更新日期:2021-07-27
down
wechat
bug