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 …

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 …

The rise of graph-structured data such as social networks, regulatory networks, citation
graphs, and functional brain networks, in combination with resounding success of deep …

We propose a generative adversarial network for point cloud upsampling, which can not
only make the upsampled points evenly distributed on the underlying surface but also …

Network topology inference is a significant problem in network science. Most graph signal
processing (GSP) efforts to date assume that the underlying network is known and then …

Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …

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 …

Two architectures that generalize convolutional neural networks (CNNs) for the processing
of signals supported on graphs are introduced. We start with the selection graph neural …

Abstract Machine learning methods, specifically, convolutional neural networks (CNNs),
have emerged as an integral part of scientific research in many disciplines. However, these …