Least squares estimation of spatial autoregressive models for large-scale social networks
MetadataShow full item record
PublisherINST MATHEMATICAL STATISTICS
CitationHuang, D., Lan, W., Zhang, H. H., & Wang, H. (2019). Least squares estimation of spatial autoregressive models for large-scale social networks. Electronic Journal of Statistics, 13(1), 1135-1165.
JournalELECTRONIC JOURNAL OF STATISTICS
Rights© 2019 Author(s). Creative Commons Attribution 4.0 International License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractDue 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.
NoteOpen Access Journal
VersionFinal published version
SponsorsNational 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]; NSF [DMS-1309507, DMS-1418172]; NSFC