Bipartite exponential random graph models with nodal random effects

被引:3
|
作者
Kevork, Sevag [1 ]
Kauermann, Goeran [1 ]
机构
[1] Ludwigs Maximilians Univ Munchen, Inst Stat, Ludwigstr 33, D-80538 Munich, Germany
关键词
Exponential random graph models; Bipartite networks; Random effects; Generalized linear mixed models; Network data analysis; P-ASTERISK MODELS; LIKELIHOOD-ESTIMATION; MAXIMUM-LIKELIHOOD; NETWORK ANALYSIS; SOCIAL NETWORKS; INFERENCE;
D O I
10.1016/j.socnet.2021.11.002
中图分类号
Q98 [人类学];
学科分类号
030303 ;
摘要
We examine the inclusion of specific nodal random effects for first- and second-mode nodes towards an ERGM for bipartite networks. The inclusion of such node-specific random effects in the ERGM accounts for unobserved heterogeneity in the bipartite network and ensures stable estimation results, especially for large-scale bipartite networks. Moreover, The predicted nodal random effects deliver reasonable interpretation to understand the network behavior. The estimation is carried out by an iterative estimation technique, iterating between pseudolikelihood estimation for the nodal random effects and maximum likelihood estimation for the network parameters.
引用
收藏
页码:90 / 99
页数:10
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