Graph Signal Processing: Overview, Challenges, and Applications

Antonio Ortega; Pascal Frossard; Jelena Kovacevic; Jose M.F. Moura; Pierre Vandergheynst

doi:10.1109/JPROC.2018.2820126

Graph Signal Processing: Overview, Challenges, and Applications

Antonio Ortega, Pascal Frossard, Jelena Kovacevic, Jose M.F. Moura, Pierre Vandergheynst

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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 in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

Original language	English (US)
Pages (from-to)	808-828
Number of pages	21
Journal	Proceedings of the IEEE
Volume	106
Issue number	5
DOIs	https://doi.org/10.1109/JPROC.2018.2820126
State	Published - May 2018

Keywords

Graph signal processing (GSP)
network science and graphs
sampling
signal processing

ASJC Scopus subject areas

General Computer Science
Electrical and Electronic Engineering

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10.1109/JPROC.2018.2820126

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title = "Graph Signal Processing: Overview, Challenges, and Applications",

abstract = "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 in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.",

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author = "Antonio Ortega and Pascal Frossard and Jelena Kovacevic and Moura, {Jose M.F.} and Pierre Vandergheynst",

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AU - Frossard, Pascal

AU - Kovacevic, Jelena

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AU - Vandergheynst, Pierre

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Graph Signal Processing: Overview, Challenges, and Applications

Abstract

Keywords

ASJC Scopus subject areas

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