标题： 社交网络分析的球面潜在空间模型 显示英文标题

标题： Spherical latent space models for social network analysis

摘要： 本文介绍了一种用于社会网络分析的球面潜在空间模型，该模型将行动者嵌入到超球面上，而不是像标准潜在空间模型那样嵌入到欧几里得空间中。 球面几何有助于表示传递关系和社区结构，自然捕捉循环模式，并确保距离有界，从而缓解传统方法中常见的退化问题。 通过马尔可夫链蒙特卡罗方法进行贝叶斯推断，以估计潜在位置和其他模型参数。 该方法使用两个基准社会网络数据集进行演示，相对于传统潜在空间模型，取得了更好的模型拟合度和可解释性。 显示英文摘要

摘要： This article introduces a spherical latent space model for social network analysis, embedding actors on a hypersphere rather than in Euclidean space as in standard latent space models. The spherical geometry facilitates the representation of transitive relationships and community structure, naturally captures cyclical patterns, and ensures bounded distances, thereby mitigating degeneracy issues common in traditional approaches. Bayesian inference is performed via Markov chain Monte Carlo methods to estimate both latent positions and other model parameters. The approach is demonstrated using two benchmark social network datasets, yielding improved model fit and interpretability relative to conventional latent space models.