recently developed approach for partitioning the nodes of a network (graph). A network with N nodes is associated to the set of … context of financial time series. Then, searching for network communities allows one to identify groups of nodes (and then …

We propose the dynamic network effect (DNE) model for the study of high-dimensional multivariate time series data … of latent stochastic network effects. The parameter-driven, nonlinear state-space model requires simulation …-section dimension is large and the network is dense. An empirical application on the spread of the COVID-19 pandemic through …

In this paper, we study the time-varying network vector autoregression (TV-NVAR) model. Constituting an ultra …

It is a challenging task to understand the complex dependency structures in an ultra-high dimensional network …, especially when one concentrates on the tail dependency. To tackle this problem, we consider a network quantile autoregression …

The complex tail dependency structure in a dynamic network with a large number of nodes is an important object to study …. Here we propose a network quantile autoregression model (NQAR), which characterizes the dynamic quantile behavior. Our NQAR … assumptions on the network structure. For this propose we develop a network Bahadur representation that gives us direct insight …