文献详情
Least squares estimation of spatial autoregressive models for large-scale social networks
文献类型：期刊
通讯作者：Lan, W (reprint author), Southwestern Univ Finance & Econ, Chengdu, Sichuan, Peoples R China.
期刊名称：ELECTRONIC JOURNAL OF STATISTICS影响因子和分区
年：2019
卷：13
期：1
页码：1135-1165
ISSN：1935-7524
关键词：Large-scale social networks; least squares estimation; network sampling; social interaction
摘要：Due to the rapid development of various social networks, the spatial autoregressive (SAR) model is becoming an important tool in social network analysis. However, major bottlenecks remain in analyzing largescale networks (e.g., Facebook has over 700 million active users), including computational scalability, estimation consistency, and proper network sampling. To address these challenges, we propose a novel least squares estimator (LSE) for analyzing large sparse networks based on the SAR model. ...More
Due to the rapid development of various social networks, the spatial autoregressive (SAR) model is becoming an important tool in social network analysis. However, major bottlenecks remain in analyzing largescale networks (e.g., Facebook has over 700 million active users), including computational scalability, estimation consistency, and proper network sampling. To address these challenges, we propose a novel least squares estimator (LSE) for analyzing large sparse networks based on the SAR model. Computationally, the LSE is linear in the network size, making it scalable to analysis of huge networks. In theory, the LSE is root n-consistent and asymptotically normal under certain regularity conditions. A new LSE-based network sampling technique is further developed, which can automatically adjust autocorrelation between sampled and unsampled units and hence guarantee valid statistical inferences. Moreover, we generalize the LSE approach for the classical SAR model to more complex networks associated with multiple sources of social interaction effect. Numerical results for simulated and real data are presented to illustrate performance of the LSE. ...Hide

DOI：10.1214/19-EJS1549
百度学术：Least squares estimation of spatial autoregressive models for large-scale social networks
语言：外文
被引频次：1
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [71532001, 11525101, 71332006, 11701560, 11401482]; Beijing Municipal Social Science Foundation [17GLC051]; Center for Applied Statistics, School of Statistics, Renmin University of China; Center of Statistical Research, Southwestern University of Finance and Economics; China's National Key Research Special Program [2016YFC0207700]; NSFNational Science Foundation (NSF) [DMS-1309507, DMS-1418172]; NSFCNational Natural Science Foundation of China [11571009]
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