Institute for Theoretical Biology, Humboldt University zu Berlin, Berlin, Germany.
Am J Primatol. 2011 Aug;73(8):731-40. doi: 10.1002/ajp.20945. Epub 2011 Mar 10.
Social network analysis (SNA) is a general heading for a collection of statistical tools that aim to describe social interactions and social structure by representing individuals and their interactions as graph objects. It was originally developed for the social sciences, but more recently it was also adopted by behavioral ecologists. However, although SNA offers a full range of exciting possibilities for the study of animal societies, some authors have raised concerns about the correct application and interpretation of network measures. In this article, we investigate how reliable and how stable network measures are (i.e. how much variation they show under re-sampling and how much they are influenced by erroneous observations). For this purpose, we took a data set of 44 nonhuman primate grooming networks and studied the effects of re-sampling at lower re-sampling rates than the originally observed ones and the inclusion of two types of errors, "mis-identification" and "mis-classification," on six different network metrics, i.e. density, degree variance, vertex strength variance, edge weight disparity, clustering coefficient, and closeness centrality. Although some measures were tolerant toward reduced sample sizes, others were sensitive and even slightly reduced samples could yield drastically different results. How strongly a metric is affected seems to depend on both the sample size and the structure of the specific network. The same general effects were found for the inclusion of sampling errors. We, therefore, emphasize the importance of calculating valid confidence intervals for network measures and, finally, we suggest a rough research plan for network studies.
社会网络分析（SNA）是一组统计工具的总称，旨在通过将个体及其相互作用表示为图形对象来描述社会互动和社会结构。它最初是为社会科学开发的，但最近也被行为生态学家采用。然而，尽管 SNA 为动物社会的研究提供了一系列令人兴奋的可能性，但一些作者对网络测量的正确应用和解释提出了担忧。在本文中，我们调查了网络测量的可靠性和稳定性（即它们在重新采样下的变化程度以及它们受错误观察影响的程度）。为此，我们使用了 44 个非人类灵长类动物梳理网络的数据，并研究了在低于原始观察到的重新采样率下进行重新采样以及包含两种类型的错误（“错误识别”和“错误分类”）对六种不同网络指标的影响，即密度、度方差、顶点强度方差、边权差异、聚类系数和接近中心性。尽管一些措施对样本量减少具有耐受性，但其他措施则较为敏感，即使是稍微减少的样本也可能产生截然不同的结果。一个度量受到多大程度的影响似乎取决于样本大小和特定网络的结构。对于包含抽样误差的情况，也发现了相同的一般影响。因此，我们强调计算网络测量有效置信区间的重要性，并最终提出了一个网络研究的大致研究计划。
