Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks
Micro-Level Interpretation of Exponential Random Graph Models with Application to Estuary Networks
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DOI：
10.1111/j.1541-0072.2012.00459.x
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发表时间：
2012-01-01
影响因子：
3.8
通讯作者：
Cranmer, Skyler J.
中科院分区：
文献类型：
--
作者：
Desmarais, Bruce A.;Cranmer, Skyler J.
指数随机图模型（ERGM）是一种日益流行的网络统计分析方法，可用于灵活分析政策参与者组织成网络的过程。通常，对ERGM结果的解释是在网络层面上进行的，这样的影响与网络结构的总体频率有关（例如，网络中封闭三角形的数量）。这限制了ERGM的效用，因为人们经常对行为者或关系层面的网络动态感兴趣，尤其是在政治和政策科学方面。在社会学和统计学中，对ERGM的微观解释已被广泛应用。我们提出了一个全面的框架来解释所有层次的ERGM分析，它将网络形成视为网络的块更新。例如，这些块可以表示每个潜在的链接、每个分体、每个参与者的进出关系或整个网络。我们将这种解释框架与网络动力学的随机行动者模型（SABM）进行了对比。我们提出了ERGM和SABM之间的理论差异，并介绍了一种在理论不够强大而无法进行先验选择时比较模型的方法。我们讨论的替代模型和我们提出的解释方法是在先前发表的关于河口政策和治理网络的数据中说明的。
The exponential random graph model (ERGM) is an increasingly popular method for the statistical analysis of networks that can be used to flexibly analyze the processes by which policy actors organize into a network. Often times, interpretation of ERGM results is conducted at the network level, such that effects are related to overall frequencies of network structures (e.g., the number of closed triangles in a network). This limits the utility of the ERGM because there is often interest, particularly in political and policy sciences, in network dynamics at the actor or relationship levels. Micro-level interpretation of the ERGM has been employed in varied applications in sociology and statistics. We present a comprehensive framework for interpretation of the ERGM at all levels of analysis, which casts network formation as block-wise updating of a network. These blocks can represent, for example, each potential link, each dyad, the out- or in-going ties of each actor, or the entire network. We contrast this interpretive framework with the stochastic actor-based model (SABM) of network dynamics. We present the theoretical differences between the ERGM and the SABM and introduce an approach to comparing the models when theory is not sufficiently strong to make the selection a priori. The alternative models we discuss and the interpretation methods we propose are illustrated on previously published data on estuary policy and governance networks.