Open AccessExponential-Family Random Graph Models for Multi-Layer Networks
Author(s) -
Pavel N. Krivitsky,
Laura M. Koehly,
Christopher Steven Marcum
Publication year - 2020
Publication title -
psychometrika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.375
H-Index - 76
eISSN - 1860-0980
pISSN - 0033-3123
DOI - 10.1007/s11336-020-09720-7
Subject(s) - exponential random graph models , exponential family , random graph , exponential distribution , exponential function , binomial (polynomial) , negative binomial distribution , set (abstract data type) , graph , mathematics , random variable , binomial distribution , computer science , statistical physics , theoretical computer science , econometrics , statistics , poisson distribution , physics , mathematical analysis , programming language
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.