Markov chain Monte Carlo exact inference for social networks

被引：12
|
作者：
McDonald, John W. [1 ]
Smith, Peter W. F. [1 ]
Forster, Jonathan J. [1 ]
机构：
[1] Univ Southampton, Southampton Stat Sci Res Inst, Southampton SO17 1BJ, Hants, England
来源：
SOCIAL NETWORKS | 2007年 / 29卷 / 01期
基金：
英国经济与社会研究理事会;
关键词：
adjacency matrices; exact conditional test; Markov chain Monte Carlo; metropolis-Hastings algorithm; reciprocity; triad census;
D O I：
10.1016/j.socnet.2006.04.003
中图分类号：
Q98 [人类学];
学科分类号：
030303 ;
摘要：
The uniformly most powerful unbiased test of reciprocity compares the observed number of mutual relations to its exact conditional distribution. Metropolis-Hastings algorithms have been proposed for generating from this distribution in order to perform Monte Carlo exact inference. Triad census statistics are often used to test for the presence of network group structure. We show how one of the proposed Metropolis-Hastings algorithms can be modified to generate from the conditional distribution of the triad census given the indegrees, the out-degrees and the number of mutual dyads. We compare the results of this algorithm with those obtained by using various approximations. (c) 2006 Elsevier B.V. All rights reserved.
引用
收藏
页码：127 / 136
页数：10
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