# Additive and multiplicative effects network models

arXiv: https://arxiv.org/abs/1807.08038 · HTML: https://mlsanity.com/paper/1807.08038/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2018.

## Built on by (most cited first, 8 of 8)

- [Mutually exciting point process graphs for modelling dynamic networks](https://mlsanity.com/paper/2102.06527/) (2021)
- [Continuous Latent Position Models for Instantaneous Interactions](https://mlsanity.com/paper/2103.17146/) (2021)
- [Identifying Peer Influence in Therapeutic Communities Adjusting for Latent Homophily](https://mlsanity.com/paper/2203.14223/) (2022)
- [Confidence Intervals for Error Rates in 1:1 Matching Tasks: Critical Statistical Analysis and Recommendations](https://mlsanity.com/paper/2306.01198/) (2023)
- [The co-varying ties between networks and item responses via latent variables](https://mlsanity.com/paper/2409.19400/) (2024)
- [Tensor State Space-based Dynamic Multilayer Network Modeling](https://mlsanity.com/paper/2506.02413/) (2025)
- [Approximate Bayesian inference for cumulative probit regression models](https://mlsanity.com/paper/2511.06967/) (2025)
- [Subzero matrix completion for sparse data analysis: large-scale learning of latent low-rank structure](https://mlsanity.com/paper/2608.21607/) (2026)

## Abstract (arXiv)

Network datasets typically exhibit certain types of statistical dependencies, such as within-dyad correlation, row and column heterogeneity, and third-order dependence patterns such as transitivity and clustering. The first two of these can be well-represented statistically with a social relations model, a type of additive random effects model originally developed for continuous dyadic data. Third-order patterns can be represented with multiplicative random effects models, which are related to matrix decompositions commonly used for matrix-variate data analysis. Additionally, these multiplicative random effects models generalize other popular latent variable 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 random effects and accommodates a variety of network data types, including continuous, binary and ordinal network relations.