A recurrent finding in personal network research is that individual and social outcomes are influenced not just by the kind of people one knows, but also by how those people are connected to each other. Personal network structure – the way in which one’s personal contacts know and interact with each other – reflects broader trends in social organization and personal communities, and shapes patterns of social capital, support, and isolation. This article proposes a method to identify typologies of structure in large collections of personal networks. The method is applied to six datasets collected in widely different circumstances and using various survey instruments. It is then compared with another recently introduced method to extract typologies of egocentric network structure. Findings show that personal network structure can be effectively summarized using just three measures of cohesive subgroup characteristics. Structural typologies can then be identified by applying standard cluster analysis techniques to the three variables. Both methods considered in the article capture significant variation in network structures, but they also show substantial levels of disagreement and cross-classification. I discuss similarities and differences between the methods, and potential applications of the proposed typologies to substantive research on personal communities, social support, and social capital.

中文翻译：

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个人网络中的结构：构建和比较类型

个人网络研究中反复出现的一个发现是，个人和社会结果不仅受到人们认识的人的影响，而且还受到这些人相互联系方式的影响。个人网络结构——个人联系人相互了解和互动的方式——反映了社会组织和个人社区的更广泛趋势，并塑造了社会资本、支持和孤立的模式。本文提出了一种识别大量个人网络中结构类型的方法。该方法适用于在广泛不同的环境中收集的六个数据集，并使用各种调查工具。然后将其与另一种最近引入的方法进行比较，以提取以自我为中心的网络结构的类型。研究结果表明，仅使用三个内聚子组特征的度量就可以有效地总结个人网络结构。然后可以通过对三个变量应用标准聚类分析技术来识别结构类型。文章中考虑的两种方法都捕捉到了网络结构的显着变化，但它们也显示出大量的分歧和交叉分类。我讨论了方法之间的异同，以及所提出的类型学在个人社区、社会支持和社会资本的实质性研究中的潜在应用。文章中考虑的两种方法都捕捉到了网络结构的显着变化，但它们也显示出大量的分歧和交叉分类。我讨论了方法之间的异同，以及所提出的类型学在个人社区、社会支持和社会资本的实质性研究中的潜在应用。文章中考虑的两种方法都捕捉到了网络结构的显着变化，但它们也显示出大量的分歧和交叉分类。我讨论了方法之间的异同，以及所提出的类型学在个人社区、社会支持和社会资本的实质性研究中的潜在应用。