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 …

This article provides an overview of the current landscape of signal processing (SP) on
directed graphs (digraphs). Directionality is inherent to many real-world (information …

Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of
graph signals, exhibiting success in recommender systems, power outage prediction, and …

This paper considers the design of optimal resource allocation policies in wireless
communication systems, which are generically modeled as a functional optimization …

Graph neural networks (GNNs) rely on graph convolutions to extract local features from
network data. These graph convolutions combine information from adjacent nodes using …

Graph processes exhibit a temporal structure determined by the sequence index and and a
spatial structure determined by the graph support. To learn from graph processes, an …

False data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent
efforts have focused on develo** machine learning (ML)-based defense strategies against …

Graph neural networks (GNNs) are information processing architectures for signals
supported on graphs. They are presented here as generalizations of convolutional neural …

Recent studies have shown that Graph Convolutional Networks (GCNs) are vulnerable to
adversarial attacks on the graph structure. Although multiple works have been proposed to …

Graphons are infinite-dimensional objects that represent the limit of convergent sequences
of graphs as their number of nodes goes to infinity. This paper derives a theory of graphon …