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arXiv 0807.3089q-bio.GNphysics.soc-ph
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人类社交网络中的遗传变异模型

Model of Genetic Variation in Human Social Networks

James H. Fowler, Christopher T. Dawes, Nicholas A. Christakis

摘要:

社交网络在各种各样的人类环境中表现出惊人的系统模式。虽然遗传变异在许多复杂的社会行为中占了很大一部分,但自我中心的社会网络属性的遗传性是未知的。在这里,我们表明,这些属性(入度,传递性和中心性)是可遗传的。然后,我们开发了一个“镜像网络”的方法来测试现存的网络模型,并表明没有帐户观察到的遗传变异,在人类社交网络。我们提出了一种替代的“吸引和引入”模型,具有两种简单形式的异质性,产生显着的遗传性以及其他重要的网络功能。我们表明,该模型是非常适合于真正的社交网络在人类。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。他们还认为,对网络属性的内在变化进行建模,对于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可能很重要。

英文摘要:

Social networks exhibit strikingly systematic patterns across a wide range of human contexts. While 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 three 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 accounts 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 as well as 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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