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 …

Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …

The underlying theme of this paper is to explore the various facets of power systems data
through the lens of graph signal processing (GSP), laying down the foundations of the Grid …

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 …

Graph signal processing (GSP) deals with the representation, analysis, and processing of
structured data, ie graph signals that are defined on the vertex set of a generic graph. A …

Graph-based models and contrastive learning have emerged as prominent methods in
Collaborative Filtering (CF). While many existing models in CF incorporate these methods in …

Graph-structured data arise in a variety of real-world context ranging from sensor and
transportation to biological and social networks. As a ubiquitous tool to process graph …

Convolutional neural networks have been successfully extended to operate on graphs,
giving rise to Graph Neural Networks (GNNs). GNNs combine information from adjacent …

We consider the problem of recovering random graph signals from nonlinear
measurements. For this setting, closed-form Bayesian estimators are usually intractable and …

Collaborative Filtering (CF) is a pivotal research area in recommender systems that
capitalizes on collaborative similarities between users and items to provide personalized …