Modeling multivariate time series (MTS) is critical in modern intelligent systems. The
accurate forecast of MTS data is still challenging due to the complicated latent variable …

Vector autoregressive (VAR) model is widely used to model time-varying processes, but it
suffers from prohibitive growth of the parameters when the number of time series exceeds a …

Modeling dynamic interactions among network components is crucial to uncovering the
evolution mechanisms of complex networks. Recently, spatio-temporal graph learning …

As irregularly structured data representations, graphs have received a large amount of
attention in recent years and have been widely applied to various real-world scenarios such …

An online algorithm for missing data imputation for networks with signals defined on the
edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we …

In this paper, we endeavor to jointly learn multiple distinct but related graphs by exploiting
the underlying topological relationships between them. The difficulty lies in how to design a …

Inferring graph topologies from data is crucial in many graph-related applications. Existing
works typically assume that signals are observed at all nodes, which may not hold due to …

Covariance Neural Networks (VNNs) perform graph convolutions on the covariance matrix
of tabular data and achieve success in a variety of applications. However, the empirical …

Learning graph structures from a collection of smooth graph signals is a fundamental
problem in data analysis and has attracted much interest in recent years. Although various …

Recent studies in multivariate time series (MTS) forecasting reveal that explicitly modeling
the hidden dependencies among different time series can yield promising forecasting …