AG Marques, S Segarra… - IEEE Signal Processing …, 2020 - ieeexplore.ieee.org
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 …
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 …
The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural …
The goal of this paper is to establish the fundamental tools to analyze signals defined over a topological space, ie a set of points along with a set of neighborhood relations. This setup …
Contemporary power grids are being challenged by rapid and sizeable voltage fluctuations that are caused by large-scale deployment of renewable generators, electric vehicles, and …
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 …
Robust screening of materials on the basis of structure–property–activity relationships to discover active photocatalysts is a highly sought out aspect of photocatalysis research …
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 …
Modern data analytics applications on graphs often operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem …