| 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.
More research on graph signal processing tries to process data on irregular graph domains, readers could refer to REF for an overview, which summarizes recent development of basic graph signal processing tools, including methods for sampling, filtering and graph learning.
Graph Signal Processing: Overview, Challenges, and Applications
Proceedings of the IEEE
Proceedings of the IEEE
Computer Science Engineering