Graph Signal Processing: History, Development, Impact, and Outlook

Abstract

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains. In this article, we provide an overview of the evolution of GSP, from its origins to the challenges ahead. The first half is devoted to reviewing the history of GSP and explaining how it gave rise to an encompassing framework that shares multiple similarities with SP. A key message is that GSP has been critical to develop novel and technically sound tools, theory, and algorithms that, by leveraging analogies with and the insights of digital SP, provide new ways to analyze, process, and learn from graph signals. In the second half, we shift focus to review the impact of GSP on other disciplines. First, we look at the use of GSP in data science problems, including graph learning and graph-based deep learning. Second, we discuss the impact of GSP on applications, including neuroscience and image and video processing. We conclude with a brief discussion of the emerging and future directions of GSP.

References in corpus (18)

• Epidemic spreading in scale-free networks
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Geometric deep learning: going beyond Euclidean data
• Networks beyond pairwise interactions: structure and dynamics
• Discrete Signal Processing on Graphs
• Discrete Signal Processing on Graphs: Sampling Theory
• Learning graphs from data: A signal representation perspective
• Connecting the Dots: Identifying Network Structure via Graph Signal Processing
• Learning Structural Node Embeddings Via Diffusion Wavelets
• Autoregressive Moving Average Graph Filtering
• Stationary signal processing on graphs
• Stationary Graph Processes and Spectral Estimation
• Signal Processing on Graphs: Causal Modeling of Unstructured Data
• Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyond
• Sampling Signals on Graphs: From Theory to Applications
• Forecasting Time Series with VARMA Recursions on Graphs
• Blind Identification of Graph Filters
• Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison (Extended Cut)

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