Graph Signal Processing: Overview, Challenges and Applications

TL;DR

This paper provides a comprehensive overview of Graph Signal Processing, highlighting recent developments, applications across various fields, and the connection to traditional signal processing, emphasizing its interdisciplinary nature and ongoing challenges.

Contribution

It offers a detailed summary of core GSP concepts, recent tools, and diverse applications, bridging GSP with classical signal processing and other research areas.

Findings

GSP tools for sampling, filtering, and graph learning have advanced significantly.

Applications include sensor networks, biological data analysis, and image processing.

Historical perspective links GSP developments to prior research in related fields.

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. We then summarize recent developments 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. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.