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指数随机图模型 （Exponential Random Graph Models，ERGM），也称为 p* 模型，是一种用于分析网络数据的统计模型。 该模型通过考虑图中节点和边之间的依赖关系，来捕捉网络结构的复杂性。 They can be used for both estimation from and simulation of dynamic network data. Tergm is used for finding temporal ergms' (tergms) and separable temporal ergms' (stergms) conditional mle (cmle) (krivitsky and handcock, 2010) and equilibrium generalized method of moments estimator (egmme) (krivitsky, 2009).
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时序指数随机图模型 (Temporal Exponential Random Graph Models，简称 TERGM) 是一种用于分析动态网络的统计模型，它是在指数随机图模型 (Exponential Random Graph Models，简称 ERGM) 的基础上发展而来。 Tergms are a broad, flexible class of models for representing the structure and dynamics observed in temporal networks Temporal exponential random graph models (tergm) are powerful statistical models that can be used to infer the temporal pattern of edge formation and elimination in complex networks (e.g., social networks).
文章浏览阅读57次。<think>好的，我现在需要帮助用户找到关于TERGM模型的详细代码实现和操作步骤，特别是在Python或R中的实现。首先，我要确认TERGM是什么。TERGM是时间指数随机图模型（Temporal Exponential Random Graph Model），用于分析动态网络数据，常见于社交网络分析中
Temporal exponential random graph models (tergm) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or markov chain monte carlo maximum likelihood (mcmc mle).
