Goodness of fit of social network models
Goodness of fit of social network models
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DOI：
10.1198/016214507000000446
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发表时间：
2008-03-01
影响因子：
3.7
通讯作者：
Handcock, Mark S.
中科院分区：
文献类型：
--
作者：
Hunter, David R.;Goodreau, Steven M.;Handcock, Mark S.
我们提出了一个系统的检查一个真实的网络数据集，使用最大似然估计指数随机图模型以及新的程序来评估模型如何拟合所观察到的网络。这些过程将观察到的网络的结构统计数据与从拟合模型模拟的网络上的相应统计数据进行比较。我们采用这种方法来研究高中生之间的友谊关系的国家青少年健康纵向研究（AddHealth）。我们主要关注一个205个节点的特定网络，尽管我们也证明了这种方法可以应用于AddHealth研究中最大的网络，有2，209个节点。我们认为，在网络文献中的几个良好的研究模型并不适合这些数据，并证明，适合显着提高时，模型包括最近开发的几何加权edgewise共享的合作伙伴，几何加权二元共享的合作伙伴，和几何加权度网络统计。我们的结论是，这些模型捕捉到了青少年友谊关系的社会结构的方面，而不是由以前的模型。
We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare structural statistics of the observed network with the corresponding statistics on networks simulated from the fitted model. We apply this approach to the study of friendship relations among high school students from the National Longitudinal Study of Adolescent Health (AddHealth). We focus primarily on one particular network of 205 nodes, although we also demonstrate that this method may be applied to the largest network in the AddHealth study, with 2,209 nodes. We argue that several well-studied models in the networks literature do not fit these data well and demonstrate that the fit improves dramatically when the models include the recently developed geometrically weighted edgewise shared partner, geometrically weighted dyadic shared partner, and geometrically weighted degree network statistics. We conclude that these models capture aspects of the social structure of adolescent friendship relations not represented by previous models.