Discrete temporal models of social networks

被引：294

作者：

Hanneke, Steve ^{[2
]}

Fu, Wenjie ^{[1
]}

Xing, Eric P. ^{[1
]}

机构：

[1] Carnegie Mellon Univ, Sch Comp Sci, Pittsburgh, PA 15213 USA

[2] Carnegie Mellon Univ, Dept Stat, Pittsburgh, PA 15213 USA

来源：

ELECTRONIC JOURNAL OF STATISTICS | 2010年 / 4卷

关键词：

D O I：

10.1214/09-EJS548

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readily adapted for these models, including maxim urn likelihood estimation algorithms. We discuss models of this type and their properties, and give examples, as well as a demonstration of their use for hypothesis testing and classification. We believe our temporal ERG models represent a useful new framework for modeling time-evolving social networks, and rewiring networks from other domains such as gene regulation circuitry, and communication networks.

引用

页码：585 / 605

页数：21