指数随机图模型
随机图
计算机科学
图形
数学
指数族
指数函数
学位分布
马尔可夫链
算法
作者
Pavel N. Krivitsky,Laura M. Koehly,Christopher Steven Marcum
出处
期刊:Psychometrika
[Springer Nature]
日期:2020-10-06
卷期号:85 (3): 630-659
被引量:4
标识
DOI:10.1007/s11336-020-09720-7
摘要
Multi-layer networks arise when more than one type of relation is observed on a common set of actors. Modeling such networks within the exponential-family random graph (ERG) framework has been previously limited to special cases and, in particular, to dependence arising from just two layers. Extensions to ERGMs are introduced to address these limitations: Conway-Maxwell-Binomial distribution to model the marginal dependence among multiple layers; a layer logic language to translate familiar ERGM effects to substantively meaningful interactions of observed layers; and nondegenerate triadic and degree effects. The developments are demonstrated on two previously published datasets.
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