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arXiv · 2503.14697

Bayesian Sociality Models: A Scalable and Flexible Alternative for Network Analysis

Juan Sosa·Carlo Martínez
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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.

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Juan Sosa, Carlo Martínez. 2025-03-18. Bayesian Sociality Models: A Scalable and Flexible Alternative for Network Analysis. https://arxiv.org/abs/2503.14697

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