Zhuoye Han | Searcharxiv

arXiv2025

Measuring Interlayer Dependence of Large Degrees in Multilayer Inhomogeneous Random Graphs

Accurately capturing interlayer dependence is essential for understanding the structure of complex multilayer networks. We propose an upper tail dependence estimator specifically designed for multilayer networks, leveraging multilayer inhomogeneous random graphs and multivariate regular variation to model extremal dependence. We establish the consistency of the estimator and demonstrate its practical effectiveness through real-data analysis of Reddit. Our findings reveal how financial market dynamics influence user interactions in the BitcoinMarkets subreddit and how seasonal trends shape engagement in sports-related subreddits. This work provides a rigorous and practical tool for quantifying extremal dependence across network layers, offering valuable insights into risk propagation and interaction patterns in multilayer systems.

stat.AP↗

arXiv2023

Multilayer Network Regression with Eigenvector Centrality and Community Structure

In the analysis of complex networks, centrality measures and community structures play pivotal roles. For multilayer networks, a critical challenge lies in effectively integrating information across diverse layers while accounting for the dependence structures both within and between layers. We propose an innovative two-stage regression model for multilayer networks, combining eigenvector centrality and network community structure within fourth-order tensor-like multilayer networks. We develop new community-based centrality measures, integrated into a regression framework. To address the inherent noise in network data, we conduct separate analyses of centrality measures with and without measurement errors and establish consistency for the least squares estimates in the regression model. The proposed methodology is applied to the world input-output dataset, investigating how input-output network data among different countries and industries influence the gross output of each industry.

stat.ME↗