指数随机图模型
指数族
指数函数
直觉
数学
指数分布
随机图
一般化
统计的
统计
图形
计量经济学
离散数学
心理学
数学分析
认知科学
出处
期刊:Social Networks
[Elsevier BV]
日期:2007-05-01
卷期号:29 (2): 216-230
被引量:398
标识
DOI:10.1016/j.socnet.2006.08.005
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI