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 …

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex
network using graph filter is presented. For conventional computation method, it needs to …

Graph neural networks (GNNs) model nonlinear representations in graph data with
applications in distributed agent coordination, control, and planning among others. Current …

IoT devices play a fundamental role in the machine learning (ML) application pipeline, as
they collect rich data for model training using sensors. However, this process can be affected …

Stochastic graph neural networks (SGNNs) are information processing architectures that
learn representations from data over random graphs. SGNNs are trained with respect to the …

One of the main challenges of graph filters is the stability of their design. While classical
graph filters allow for a stable design using optimal polynomial approximation theory …

Our goal in this paper is the robust design of filters acting on signals observed over graphs
subject to small perturbations of their edges. The focus is on develo** a method to identify …

A polynomial graph filter is one of the widely used systems for processing irregular data
collected from various complex networks. To increase the flexibility of the conventional …

By replacing classical convolutions with graph filters, graph convolutional neural networks
(GNNs) have emerged as powerful tools to learn a nonlinear map** for data defined over …