Time: Tuesday, 24/June/2025: 9:00am - 12:00pm Session Chair: Anni Hong Session Chair: Nynke Niezink |
Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. Consequently, there is a growing demand for analytical methods and tools for these network structures. This workshop offers a practical introduction to the multip2 R package for analyzing multiplex network data. Participants will learn the essentials of our Bayesian multiplex mixed-effects network model in the p2 (van Duijn et al., 2004) modeling framework and gain hands-on experience with the entire workflow, from data wrangling to model interpretation and assessment through a data example. The workshop will enable participants to model cross-layer dyadic dependencies as fixed effects and actor-specific dependencies as random effects, while also considering the influence of covariates in the analysis of cross-sectional, directed binary multiplex network data.
topics includes:
– Introduction to the multiplex p2 modeling framework
– a brief introduction to Bayesian analysis
– Overview of the R package multiP2 and the underlying estimation procedure in stan
– Data preparation
– Picking priors via prior predictive checks
– Model fitting and convergence diagnostics
– Interpretation of model coefficients
– Goodness-of-fit assessment via simulations and plotting
Note: participants are expected to have a basic familiarity with R for the practical segment of the workshop and some understanding of statistical inference for the conceptual portion.
Expected length: 3 hr, Max attendance: 20
Multiplex social network analysis with multip2
Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. Consequently, there is a growing demand for analytical methods and tools for these network structures. This workshop offers a practical introduction to the multip2 R package for analyzing multiplex network data. Participants will learn the essentials of our Bayesian multiplex mixed-effects network model in the p2 (van Duijn et al., 2004) modeling framework and gain hands-on experience with the entire workflow, from data wrangling to model interpretation and assessment through a data example. The workshop will enable participants to model cross-layer dyadic dependencies as fixed effects and actor-specific dependencies as random effects, while also considering the influence of covariates in the analysis of cross-sectional, directed binary multiplex network data.
topics includes:
– Introduction to the multiplex p2 modeling framework
– a brief introduction to Bayesian analysis
– Overview of the R package multiP2 and the underlying estimation procedure in stan
– Data preparation
– Picking priors via prior predictive checks
– Model fitting and convergence diagnostics
– Interpretation of model coefficients
– Goodness-of-fit assessment via simulations and plotting
Note: participants are expected to have a basic familiarity with R for the practical segment of the workshop and some understanding of statistical inference for the conceptual portion.
Expected length: 3 hr, Max attendance: 20