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Subnetwork Estimation for Spatial Autoregressive Models in Large-scale Networks by Li, Xuetong; Wang, Feifei; Lan, Wei; Wang, Hansheng is a scholarly article available to read on EtoBox.

What is Subnetwork Estimation for Spatial Autoregressive Models in Large-scale Networks about?

Large-scale networks are commonly encountered in practice (e.g., Facebook and Twitter) by researchers. In order to study the network interaction between different nodes of large-scale networks, the spatial autoregressive (SAR) model has been popularly employed. Despite its popularity, the estimation of a SAR model on large-scale networks remains very challenging. On the one hand, due to policy limitations or high collection costs, it is often impossible for independent researchers to observe or collect all network information. On the other hand, even if the entire network is accessible, estimating the SAR model using the quasi-maximum likelihood estimator (QMLE) could be computationally infeasible due to its high computational cost. To address these challenges, we propose here a subnetwork estimation method based on QMLE for the SAR model. By using appropriate sampling methods, a subnetwork, consisting of a much-reduced number of nodes, can be constructed. Subsequently, the standard QMLE can be computed by treating the sampled subnetwork as if it were the entire network. This leads to a significant reduction in information collection and model computation costs, which increases the

Author

Li, Xuetong; Wang, Feifei; Lan, Wei; Wang, Hansheng

Published

2023

Language

EN