Statistical Inference in a Directed Network Model With Covariates

• 成果类型: Article
• 署名作者: Yan, Ting; Jiang, Binyan; Fienberg, Stephen E.; Leng, Chenlei
• 署名单位: Central China Normal University; Hong Kong Polytechnic University; Carnegie Mellon University; University of Warwick; Alan Turing Institute
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1448829
• 发表日期: 2019
• 页码: 857-868
• 关键词: p-asterisk models; stochastic blockmodels; Random graphs; beta-model; asymptotics; number

摘要

Networks are often characterized by node heterogeneity for which nodes exhibit different degrees of interaction and link homophily for which nodes sharing common features tend to associate with each other. In this article, we rigorously study a directed network model that captures the former via node-specific parameterization and the latter by incorporating covariates. In particular, this model quantifies the extent of heterogeneity in terms of outgoingness and incomingness of each node by different parameters, thus allowing the number of heterogeneity parameters to be twice the number of nodes. We study the maximum likelihood estimation of the model and establish the uniform consistency and asymptotic normality of the resulting estimators. Numerical studies demonstrate our theoretical findings and two data analyses confirm the usefulness of our model. Supplementary materials for this article are available online.