Bayesian Sociality Models: A Scalable and Flexible Alternative for Network Analysis
Abstract
Bayesian sociality models provide a scalable and flexible alternative for network analysis, capturing degree heterogeneity through actor-specific parameters while mitigating the identifiability challenges of latent space models. This paper develops a comprehensive Bayesian inference framework, leveraging Markov chain Monte Carlo and variational inference to assess their efficiency-accuracy trade-offs. Through empirical and simulation studies, we demonstrate the model’s robustness in goodness-of-fit, predictive performance, clustering, and other key network analysis tasks. The Bayesian paradigm further enhances uncertainty quantification and interpretability, positioning sociality models as a powerful and generalizable tool for modern network science.
Fields of Study & Subjects
Complex Network Analysis Techniques
Bayesian Modeling and Causal Inference
Markov Chains and Monte Carlo Methods
Publication details
- Journal / source
- Journal of Computational and Graphical Statistics
- Volume
- 35
- Issue
- 3
- Pages
- 1439–1459
- Publication date
- 10 February 2026
- Language
- EN