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.

Keywords

Computer scienceSignal processingGraphData scienceData processingDigital signal processingArtificial intelligenceMachine learningTheoretical computer scienceDatabase

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Publication Info

Year
2018
Type
article
Volume
106
Issue
5
Pages
808-828
Citations
1607
Access
Closed

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Cite This

Antonio Ortega, Pascal Frossard, Jelena Kovačević et al. (2018). Graph Signal Processing: Overview, Challenges, and Applications. Proceedings of the IEEE , 106 (5) , 808-828. https://doi.org/10.1109/jproc.2018.2820126

Identifiers

DOI
10.1109/jproc.2018.2820126
arXiv
1712.00468

Data Quality

Data completeness: 84%