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 the 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.

中文翻译：

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用于机器学习的图信号处理：回顾和新视角

大规模结构化数据的有效表示、处理、分析和可视化，尤其是与复杂领域（如网络和图）相关的数据，是现代机器学习的关键问题之一。图信号处理 (GSP) 是信号处理模型和算法的一个充满活力的分支，旨在处理图支持的数据，为应对这一挑战开辟了新的研究途径。在本文中，我们回顾了 GSP 概念和工具（例如图形过滤器和变换）对开发新型机器学习算法的一些重要贡献。具体而言，我们的讨论集中在以下三个方面：利用数据结构和关系先验，提高数据和计算效率，以及增强模型的可解释性。此外，我们为 GSP 技术的未来发展提供了新的视角，该技术一方面可以作为应用数学和信号处理与另一方面的机器学习和网络科学之间的桥梁。跨这些不同学科的交叉融合可能有助于解决现代复杂数据分析的众多挑战。