This paper presents a systemic review of the contributions that stochastic actor‐oriented models (SAOMs) and exponential random graph models (ERGMs) have made to the study of industrial clusters and agglomeration processes. Results show that ERGMs and SAOMs are especially popular to study network evolution, proximity dynamics and multiplexity. The paper concludes that although these models have advanced the field by enabling empirical testing of a number of theories, they often operationalize the same theory in completely different ways, making it difficult to draw conclusions that can be generalized beyond the particular case studies on which each paper is based. The paper ends with suggestions of ways to address this problem.

中文翻译：

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统计网络模型对聚类及其演化的研究的贡献

本文系统地回顾了随机行为者导向模型（SAOM）和指数随机图模型（ERGM）对产业集群和集聚过程的研究。结果表明，ERGM和SAOM在研究网络演进，邻近动态和复用性方面特别受欢迎。本文的结论是，尽管这些模型通过对多种理论进行实证检验而在该领域取得了进步，但它们通常以完全不同的方式来操作同一理论，因此很难得出可以推广到每个案例所基于的特定案例研究以外的结论的结论。纸张是根据的。本文最后提出了解决此问题的方法的建议。