Fowler James H, Dawes Christopher T, Christakis Nicholas A
Department of Political Science, University of California, San Diego, CA 92093, USA.
Proc Natl Acad Sci U S A. 2009 Feb 10;106(6):1720-4. doi: 10.1073/pnas.0806746106. Epub 2009 Jan 26.
Social networks exhibit strikingly systematic patterns across a wide range of human contexts. Although genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here, we show that 3 of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none account for observed genetic variation in human social networks. We propose an alternative "Attract and Introduce" model with two simple forms of heterogeneity that generates significant heritability and other important network features. We show that the model is well suited to real social networks in humans. These results suggest that natural selection may have played a role in the evolution of social networks. They also suggest that modeling intrinsic variation in network attributes may be important for understanding the way genes affect human behaviors and the way these behaviors spread from person to person.
社交网络在广泛的人类情境中呈现出显著的系统性模式。尽管基因变异在许多复杂社会行为的变异中占很大一部分，但以自我为中心的社交网络属性的遗传力尚不清楚。在这里，我们表明其中3个属性（入度、传递性和中心性）是可遗传的。然后，我们开发了一种“镜像网络”方法来测试现有的网络模型，结果表明没有一个模型能够解释人类社交网络中观察到的基因变异。我们提出了一种具有两种简单异质性形式的替代“吸引与引入”模型，该模型产生了显著的遗传力和其他重要的网络特征。我们表明该模型非常适合人类真实的社交网络。这些结果表明，自然选择可能在社交网络的进化中发挥了作用。它们还表明，对网络属性的内在变异进行建模可能对理解基因影响人类行为的方式以及这些行为在人与人之间传播的方式很重要。
