Curved exponential family models for social networks

被引:328
作者
Hunter, David R. [1 ]
机构
[1] Penn State Univ, University Pk, PA 16802 USA
关键词
exponential random graph model; maximum likelihood estimation; p-Star model;
D O I
10.1016/j.socnet.2006.08.005
中图分类号
Q98 [人类学];
学科分类号
030303 ;
摘要
Curved exponential family models are a useful generalization of exponential random graph models (ERGMs). In particular, models involving the alternating k-star, alternating k-triangle, and alternating k-twopath statistics of Snijders et al. [Snijders, T.A.B., Pattison, P.E., Robins, G.L., Handcock, M.S., in press. New specifications for exponential random graph models. Sociological Methodology] may be viewed as curved exponential family models. This article unifies recent material in the literature regarding curved exponential family models for networks in general and models involving these alternating statistics in particular. It also discusses the intuition behind rewriting the three alternating statistics in terms of the degree distribution and the recently introduced shared partner distributions. This intuition suggests a redefinition of the alternating k-star statistic. Finally, this article demonstrates the use of the statnet package in R for fitting models of this sort, comparing new results on an oft-studied network dataset with results found in the literature. (c) 2006 Elsevier B. V. All rights reserved.
引用
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页码:216 / 230
页数:15
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