Graph Signal Processing: Overview, Challenges, and Applications

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

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Pages (from-to)808-828
Number of pages21
JournalProceedings of the IEEE
Issue number5
StatePublished - May 2018


  • 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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