Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Hum Behav. 2024 Jun;8(6):1057-1064. doi: 10.1038/s41562-024-01865-0. Epub 2024 Apr 22.
In widely used models of biological contagion, interventions that randomly rewire edges (generally making them 'longer') accelerate spread. However, recent work has argued that highly clustered, rather than random, networks facilitate the spread of threshold-based contagions, such as those motivated by myopic best response for adoption of new innovations, norms and products in games of strategic complement. Here we show that minor modifications to this model reverse this result, thereby harmonizing qualitative facts about how network structure affects contagion. We analyse the rate of spread over circular lattices with rewired edges and show that having a small probability of adoption below the threshold probability is enough to ensure that random rewiring accelerates the spread of a noisy threshold-based contagion. This conclusion is verified in simulations of empirical networks and remains valid with partial but frequent enough rewiring and when adoption decisions are reversible but infrequently so, as well as in high-dimensional lattice structures.
在广泛应用的生物传播模型中，随机重连边（通常使边“更长”）的干预措施会加速传播。然而，最近的研究认为，高度聚类的网络而不是随机网络促进了基于阈值的传播，例如基于近视最佳响应的传播，这种传播推动了战略互补博弈中对新创新、规范和产品的采用。在这里，我们表明，对该模型的微小修改会扭转这一结果，从而协调网络结构如何影响传播的定性事实。我们分析了具有重连边的圆形晶格上的传播速度，并表明在阈值概率以下有一个很小的采用概率就足以确保随机重连加速基于噪声的阈值传播。这一结论在经验网络的模拟中得到了验证，并且在部分但足够频繁的重连以及采用决策可逆但不太频繁的情况下仍然有效，在高维晶格结构中也是如此。
