Regression of directed graphs on independent effects for density and reciprocity

IF 1.3 4区社会学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Mathematical Sociology Pub Date : 2017-10-02 DOI:10.1080/0022250X.2017.1387858

B. Zijlstra

引用次数: 5

Abstract

ABSTRACT In common models for dyadic network regression, the density and reciprocity parameters are dependent on each other. Here, the j1 and j2 models are introduced with a density parameter that represents the log odds of a single tie. Consequently, the density and reciprocity parameters are independent and the interpretation of both parameters more straightforward. Estimation procedures and simulation results for these new models are discussed as well as an illustrative example.

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关于密度和互易的独立效应的有向图回归

摘要在常见的二元网络回归模型中，密度和互易参数是相互依赖的。这里，j1和j2模型引入了一个密度参数，该参数表示单个平局的对数几率。因此，密度和互易性参数是独立的，并且对这两个参数的解释更为直接。讨论了这些新模型的估计过程和仿真结果，并举例说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Mathematical Sociology 数学-数学跨学科应用

CiteScore

2.90

自引率

10.00%

发文量

审稿时长

>12 weeks

期刊介绍： The goal of the Journal of Mathematical Sociology is to publish models and mathematical techniques that would likely be useful to professional sociologists. The Journal also welcomes papers of mutual interest to social scientists and other social and behavioral scientists, as well as papers by non-social scientists that may encourage fruitful connections between sociology and other disciplines. Reviews of new or developing areas of mathematics and mathematical modeling that may have significant applications in sociology will also be considered. The Journal of Mathematical Sociology is published in association with the International Network for Social Network Analysis, the Japanese Association for Mathematical Sociology, the Mathematical Sociology Section of the American Sociological Association, and the Methodology Section of the American Sociological Association.