The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …

Reconstructing time-varying graph signals (or graph time-series imputation) is a critical
problem in machine learning and signal processing with broad applications, ranging from …

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 …

The prevalence of multi-dimensional (mD) graph signals in various real-world applications,
such as digital images and data with spatial and temporal dimensions, highlights their …

Time-varying graph signal recovery has been widely used in many applications, including
climate change, environmental hazard monitoring, and epidemic studies. It is crucial to …

The modeling of time-varying graph signals as stationary time-vertex stochastic processes
permits the inference of missing signal values by efficiently employing the correlation …

This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message
passing method that simultaneously conducts online prediction, missing data imputation …

Graph signal processing (GSP) facilitates the analysis of high-dimensional data on non-
Euclidean domains by utilizing graph signals defined on graph vertices. In addition to static …

Human body motion segmentation plays a major role in many applications, ranging from
computer vision to robotics. Among a variety of algorithms, graph-based approaches have …

We propose a time-varying graph signal recovery method for estimating the true time-
varying graph signal from corrupted observations by leveraging dynamic graphs. Most of the …