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 …

In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as
spatio-temporal graphs, have achieved remarkable performance. However, existing GCN …

This article introduces a unified framework for developing graph-based change detection
(CD) algorithms in remote sensing, which is based on signal feasibility problems and …

A novel reconstruction method for compressive spectral imaging is designed by assuming
that the spectral image of interest is sufficiently smooth on a collection of graphs. Since the …

Traditional methods to measure spatio-temporal variations in above-ground biomass
dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features …

Background Characterizing the topology of gene regulatory networks (GRNs) is a
fundamental problem in systems biology. The advent of single cell technologies has made it …

We consider the problem of identifying the graph topology from a set of smooth graph
signals. A well-known approach to this problem is minimizing the Dirichlet energy …

The growing success of graph signal processing (GSP) approaches relies heavily on prior
identification of a graph over which network data admit certain regularity. However …

This work introduces a highly scalable spectral graph densification (SGL) framework for
learning resistor networks with linear measurements, such as node voltages and currents …

Graph convolutional neural networks (GCNN) have been successfully applied to many
different graph based learning tasks including node and graph classification, matrix …