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.

翻译：贝叶斯社交性模型为网络分析提供了一种可扩展且灵活的替代方案，通过参与者特定参数捕捉度异质性，同时缓解了潜在空间模型的可识别性挑战。本文构建了一个全面的贝叶斯推断框架，利用马尔可夫链蒙特卡罗方法和变分推断来评估其效率与精度的权衡关系。通过实证与仿真研究，我们证明了该模型在拟合优度、预测性能、聚类及其他关键网络分析任务中的稳健性。贝叶斯范式进一步增强了不确定性量化与可解释性，使社交性模型成为现代网络科学中强大且可泛化的工具。