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Learning Graphs From Data: A Signal Representation Perspective

Dong, Xiaowen  
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Thanou, Dorina  
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Rabbat, Michael
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May 1, 2019
IEEE Signal Processing Magazine

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this article, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP-based graph-inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine-learning algorithms for learning graphs from data.

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Errata (wrt IEEE SPM version).pdf

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Postprint

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openaccess

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113.62 KB

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Adobe PDF

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