| Session | |
WS-T33: Multiplex social network analysis with multip2
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| Session Abstract | |
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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 | |
| Presentations | |
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 | |