Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …

The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …

The goal of this paper is to establish the fundamental tools to analyze signals defined over a
topological space, ie a set of points along with a set of neighborhood relations. This setup …

To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we
propose a randomized resampling strategy that selects a representative subset of points …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Missing data imputation (MDI) is the task of replacing missing values in a dataset with
alternative, predicted ones. Because of the widespread presence of missing data, it is a …

Despite the impressive performance obtained by recent single-image hand modeling
techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This …

The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive
estimation of signals defined over graphs. Assuming the graph signal to be band-limited …

The goal of this paper is to propose novel strategies for adaptive learning of signals defined
over graphs, which are observed over a (randomly) time-varying subset of vertices. We …