Graph-guided semi-supervised learning (SSL) has gained popularity in several network
science applications, including biological, social, and financial ones. SSL becomes …

Graph-guided semi-supervised learning (SSL) and inference has emerged as an attractive
research field thanks to its documented impact in a gamut of application domains, including …

This paper investigates the problem of learning a graphical model from incomplete spatio-
temporal measurements. Our purpose is to analyze a time-varying graph signal represented …

Heat diffusion processes have found wide applications in modelling dynamical systems over
graphs. In this paper, we consider the recovery of a-bandlimited graph signal that is an initial …

Graph-based inference arises in a gamut of network science-related applications, including
smart transportation, climate forecasting, and neuroscience. Given observations over a …

Inference of time varying data over graphs is of importance in real-world applications such
as urban water networks, economics, and brain recordings. It typically relies on identifying a …

In this work, we investigate the sampling and reconstruction of spectrally $ s $-sparse
bandlimited graph signals governed by heat diffusion processes. We propose a random …

Inference of spatio-temporal processes over graphs arises in a gamut of network science-
related applications, including smart transportation, climate forecasting, and neuroscience …

Current spatiotemporal learning methods for complex data exploit the graph structure as an
inductive bias to restrict the function space and improve data and computation efficiency …

We consider the space-time sampling and reconstruction of sparse bandlimited graph
signals driven by a heat diffusion process. In this paper, we develop a sampling framework …