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Exponential-family Random Graph Models for Rank-order Relational Data
Sociological Methodology ( IF 6.118 ) Pub Date : 2017-07-06 , DOI: 10.1177/0081175017692623
Pavel N. Krivitsky 1 , Carter T. Butts 2
Affiliation  

Rank-order relational data, in which each actor ranks other actors according to some criterion, often arise from sociometric measurements of judgment or preference. The authors propose a general framework for representing such data, define a class of exponential-family models for rank-order relational structure, and derive sufficient statistics for interdependent ordinal judgments that do not require the assumption of comparability across raters. These statistics allow estimation of effects for a variety of plausible mechanisms governing rank structure, both in a cross-sectional context and evolving over time. The authors apply this framework to model the evolution of liking judgments in an acquaintance process and to model recall of relative volume of interpersonal interaction among members of a technology education program.

中文翻译:

秩序关系数据的指数族随机图模型

等级顺序关系数据,其中每个参与者根据某个标准对其他参与者进行排名,通常来自社会计量学对判断或偏好的测量。作者提出了用于表示此类数据的一般框架,为等级顺序关系结构定义了一类指数族模型,并为不需要假设评估者之间具有可比性的相互依赖的序数判断得出足够的统计数据。这些统计数据允许对控制等级结构的各种合理机制的影响进行估计,无论是在横截面背景下还是随着时间的推移而演变。作者应用这个框架来模拟熟人过程中喜好判断的演变,并模拟技术教育计划成员之间人际互动的相对量的回忆。
更新日期:2017-07-06
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