Title: Multilayer Network Regression with Eigenvector Centrality and Community Structure

Abstract: In the analysis of complex networks, centrality measures and community structures are two important aspects. For multilayer networks, one crucial task is to integrate information across different layers, especially taking the dependence structure within and between layers into consideration. In this study, we introduce a novel two-stage regression model (CC-MNetR) that leverages the eigenvector centrality and network community structure of fourth-order tensor-like multilayer networks. In particular, we construct community-based centrality measures, which are then incorporated into the regression model. In addition, considering the noise of network data, we analyze the centrality measure with and without measurement errors respectively, and establish the consistent properties of the least squares estimates in the regression. Our proposed method is then applied to the World Input-Output Database (WIOD) dataset to explore how input-output network data between different countries and different industries affect the Gross Output of each industry.