Graph signal processing for machine learning: A review and new perspectives

TL;DR

This paper reviews how graph signal processing techniques can enhance machine learning by leveraging data structure, improving efficiency, and increasing interpretability, while also proposing future interdisciplinary research directions.

Contribution

It provides a comprehensive review of GSP applications in machine learning and introduces new perspectives on integrating mathematical, signal processing, and network science approaches.

Findings

GSP techniques improve data representation and analysis on graphs.

Graph filters and transforms enable more efficient algorithms.

Future research directions include interdisciplinary integration.

Abstract

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.