Auto-logistic actor attribute models (ALAAMs) are models for analysing social influence or social contagion for cross-sectional networks, when the outcome of interest is dichotomous. If no dependencies among the outcomes of the nodes are assumed, this model reduces to logistic regression. When dependencies through the network, such as social contagion, are assumed, however, the ALAAM provides testable parameters that capture these processes.

The workshop will introduce the R package “balaam”, which provides a range of different parameters for network dependencies and estimates the model using Bayesian inference. The package also provides goodness-of-fit analysis, model selection indices, as well as principled approaches for dealing with missing outcomes.

Topics treated are:

principles of Bayesian inference; model specification; MCMC estimation for the ALAAM; model selection; missing data analysis.

Prerequisites

The workshop is intended for participants who have working knowledge of quantitative analysis and experience in empirical network research. Fundamental social network analysis skills are assumed.

Literature:

Koskinen, J. and Daraganova, G. (2022) Bayesian analysis of social influence. Journal of the Royal Statistical Society Series A: Statistics in Society 185.4, pp. 1855–1881.

Daraganova, G. & Robins, G. (2013) Autologistic actor attribute model. In: Lusher, D., Koskinen, J. & Robins,G. (Eds.) Exponential random graph models for social networks: theory, methods and applications. New York: Cambridge University Press. pp. 102–114.

Daraganova, G. & Pattison, P. (2013) Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live. In: Lusher, D., Koskinen, J. & Robins, G. (Eds.) Exponential random graph models for social networks: theory, methods and applications. New York: Cambridge University Press, pp. 237–247.

ALAAM website: https://github.com/johankoskinen/ALAAM