Graph Signal Processing: Overview, Challenges, and Applications

• Published in: Proceedings of the IEEE Vol. 106; no. 5; pp. 808 - 828
• Main Authors: Ortega, Antonio, Frossard, Pascal, Kovacevic, Jelena, Moura, Jose M. F., Vandergheynst, Pierre
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Data processing; Digital signal processing; Digital signal processors; Filtration; Graph signal processing (GSP); Graph theory; Graphical models; Image processing; Laplace equations; Machine learning; network science and graphs; sampling; Sampling methods; Signal processing; Social network services
• ISSN: 0018-9219; 1558-2256
• DOI: 10.1109/JPROC.2018.2820126

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.