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Exponential-family random graph models for rank-order relational data

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posted on 2024-11-15, 09:12 authored by Pavel Krivitsky, Carter T Butts
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.

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Citation

Krivitsky, P. N. & Butts, C. T. (2017). Exponential-family random graph models for rank-order relational data. Sociological Methodology, 47 (1), 68-112.

Journal title

Sociological Methodology

Volume

47

Issue

1

Pagination

68-112

Language

English

RIS ID

109984

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