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February 2021 Additive and Multiplicative Effects Network Models
Peter Hoff
Statist. Sci. 36(1): 34-50 (February 2021). DOI: 10.1214/19-STS757

Abstract

Network datasets typically exhibit certain types of statistical patterns, such as within-dyad correlation, degree heterogeneity, and triadic patterns such as transitivity and clustering. The first two of these can be well represented with a social relations model, a type of additive effects model originally developed for continuous dyadic data. Higher-order patterns can be represented with multiplicative effects models, which are related to matrix decompositions that are commonly used for matrix-variate data analysis. Additionally, these multiplicative effects models generalize other popular latent feature network models, such as the stochastic blockmodel and the latent space model. In this article, we review a general regression framework for the analysis of network data that combines these two types of effects, and accommodates a variety of network data types, including continuous, binary and ordinal network relations.

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Peter Hoff. "Additive and Multiplicative Effects Network Models." Statist. Sci. 36 (1) 34 - 50, February 2021. https://doi.org/10.1214/19-STS757

Information

Published: February 2021
First available in Project Euclid: 21 December 2020

MathSciNet: MR4194202
Digital Object Identifier: 10.1214/19-STS757

Rights: Copyright © 2021 Institute of Mathematical Statistics

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Vol.36 • No. 1 • February 2021
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