# Graph Signal Processing: Overview, Challenges, and Applications

@article{Ortega2018GraphSP, title={Graph Signal Processing: Overview, Challenges, and Applications}, author={Antonio Ortega and Pascal Frossard and Jelena Kovacevic and Jos{\'e} M. F. Moura and Pierre Vandergheynst}, journal={Proceedings of the IEEE}, year={2018}, volume={106}, pages={808-828} }

Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. [] Key Method 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.

## 909 Citations

### GENERALIZED GRAPH SIGNAL PROCESSING

- Computer Science2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
- 2018

This paper develops a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals and includes a hybrid framework of graph and classical signal processing over a continuous domain.

### Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries

- Computer ScienceIEEE Signal Processing Magazine
- 2020

Modern signal processing frameworks that generalize GSP to multiway data are reviewed, starting from graph signals coupled to familiar regular axes, such as time in sensor networks, and then extending to general graphs across all tensor modes.

### Graph Signal Processing over Multilayer Networks – Part II: Useful Tools and Practical Applications

- Computer Science
- 2021

This work introduces a tensor-based framework of graph signal processing over multilayer networks (M-GSP) to analyze high-dimensional signal interactions and defines the concepts of stationary process, convolution, bandlimited signals, and sampling theory over multILayer networks.

### Image Processing via Multilayer Graph Spectra

- Computer Science
- 2021

This work introduces a tensor-based framework of multilayer graph signal processing (M-G SP) and presents useful M-GSP tools for image processing and introduces several applications, including RGB image compression, edge detection and hyperspectral image segmentation.

### Graph Signal Processing for Machine Learning: A Review and New Perspectives

- Computer ScienceIEEE Signal Processing Magazine
- 2020

This article reviews a few important contributions made by GSP concepts and tools to the development of novel machine learning algorithms, and provides new perspectives on the future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing and machine learning and network science.

### Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

- Computer ScienceIEEE Signal Processing Magazine
- 2020

This work proposes the graph modularity matrix as the centerpiece of GSP to incorporate knowledge about graph community structure when processing signals on the graph but without the need for community detection and demonstrates how concepts from network science can lead to new, meaningful operations on graph signals.

### Turning Digital Signal Processing into Graph Signal Processing: Overview and Applications

- Computer Science2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan)
- 2020

An overview and some applications of a graph are presented, highlighting the application of Fourier transform, frequency analysis, filtering, sampling and data classification, and their connection to conventional digital signal processing.

### To further understand graph signals

- Computer Science
- 2022

This paper argues here that graph signals may contain hidden geometric information of the network, independent of (graph) Fourier theories, and provides a framework to understand such information.

### A Hilbert Space Theory of Generalized Graph Signal Processing

- Computer ScienceIEEE Transactions on Signal Processing
- 2019

This paper develops a concept analogous to Fourier transform for generalized GSP and the theory of filtering and sampling such signals and includes a hybrid framework of graph and classical signal processing over a continuous domain.

### Graph Signal Processing: Modulation, Convolution, and Sampling

- Computer Science
- 2019

This paper revisits modulation, convolution, and sampling of graph signals as appropriate natural extensions of the corresponding DSP concepts to form a spectral GSP theory that parallels in the graph frequency domain the existing G SP theory in the vertex domain.

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