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 …

Transformers were originally proposed as a sequence-to-sequence model for text but have
become vital for a wide range of modalities, including images, audio, video, and undirected …

The analysis of signals defined over a graph is relevant in many applications, such as social
and economic networks, big data or biological networks, and so on. A key tool for analyzing …

Congestion recognition is the prerequisite for traffic control and management, vehicle
routing, and many other applications in intelligent transportation systems. Different types of …

Basic operations in graph signal processing consist of processing signals indexed on
graphs either by filtering them or by changing their domain of representation in order to …

Abstract The area of Data Analytics on graphs promises a paradigm shift, as we approach
information processing of new classes of data which are typically acquired on irregular but …

This paper develops new designs for recommendation systems inspired by recent advances
in graph signal processing. Recommendation systems aim to predict unknown ratings by …

Markov chains are a class of probabilistic models that have achieved widespread
application in the quantitative sciences. This is in part due to their versatility, but is …

Graph signal processing is a useful tool for representing, analyzing, and processing the
signal lying on a graph, and has attracted attention in several fields including data mining …

Traffic speed is central to characterizing the fluidity of the road network. Many transportation
applications rely on it, such as real-time navigation, dynamic route planning, and congestion …