Haas School of Business, The University of California, Berkeley, Berkeley, CA, USA.
The Annenberg School for Communication, The University of Pennsylvania, Philadelphia, PA, USA.
Nat Commun. 2021 Jul 20;12(1):4430. doi: 10.1038/s41467-021-24704-6.
The standard measure of distance in social networks - average shortest path length - assumes a model of "simple" contagion, in which people only need exposure to influence from one peer to adopt the contagion. However, many social phenomena are "complex" contagions, for which people need exposure to multiple peers before they adopt. Here, we show that the classical measure of path length fails to define network connectedness and node centrality for complex contagions. Centrality measures and seeding strategies based on the classical definition of path length frequently misidentify the network features that are most effective for spreading complex contagions. To address these issues, we derive measures of complex path length and complex centrality, which significantly improve the capacity to identify the network structures and central individuals best suited for spreading complex contagions. We validate our theory using empirical data on the spread of a microfinance program in 43 rural Indian villages.
在社交网络中，标准的距离衡量指标——平均最短路径长度——假设了一种“简单”传染的模型，即人们只需要接触到一个同伴的影响就可以接受传染。然而，许多社会现象是“复杂”的传染，人们需要接触多个同伴才能接受。在这里，我们表明，经典的路径长度衡量标准无法定义复杂传染的网络连通性和节点中心性。基于路径长度的经典定义的中心性度量和播种策略经常错误地识别出对传播复杂传染最有效的网络特征。为了解决这些问题，我们推导出了复杂路径长度和复杂中心性的度量，这显著提高了识别最适合传播复杂传染的网络结构和中心个体的能力。我们使用在印度 43 个农村村庄中微额供资计划传播的实证数据验证了我们的理论。
