The autologistic actor attribute model (ALAAM) is a model for social influence, derived from the more widely known exponential-family random graph model (ERGM). ALAAMs can be used to estimate parameters corresponding to multiple forms of social contagion associated with network structure and actor covariates. This work introduces ALAAMEE, open-source Python software for estimation, simulation, and goodness-of-fit testing for ALAAM models. ALAAMEE implements both the stochastic approximation and equilibrium expectation (EE) algorithms for ALAAM parameter estimation, including estimation from snowball sampled network data. It implements data structures and statistics for undirected, directed, and bipartite networks. We use a simulation study to assess the accuracy of the EE algorithm for ALAAM parameter estimation and statistical inference, and demonstrate the use of ALAAMEE with empirical examples using both small (fewer than 100 nodes) and large (more than 10 000 nodes) networks.

翻译：自逻辑行为者属性模型（ALAAM）是一种源于更广为人知的指数族随机图模型（ERGM）的社会影响模型。ALAAM可估计与网络结构和行为者协变量相关的多种社会传染形式的参数。本文介绍ALAAMEE——用于ALAAM模型估计、模拟和拟合优度检验的开源Python软件。ALAAMEE实现了用于ALAAM参数估计的随机逼近算法和均衡期望（EE）算法，包括基于滚雪球抽样网络数据的估计。该软件支持无向网络、有向网络和二部网络的数据结构与统计量。我们通过模拟研究评估EE算法在ALAAM参数估计与统计推断中的准确性，并利用包含小型（少于100个节点）和大型（超过10，000个节点）网络的实证案例展示ALAAMEE的应用。