Bayesian Sociality Models: A Scalable and Flexible Alternative for Network Analysis
本文提出贝叶斯社交性模型，通过演员特定参数捕捉度异质性，并开发了马尔可夫链蒙特卡洛和变分推断框架，实证表明其在拟合优度、预测和聚类等任务中表现稳健，增强了不确定性量化与可解释性。
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.