The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Learning Graphs From Data: A Signal Representation Perspective
Open accessAbstract
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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Cites
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Cited by
Non-Negative Kernel Graphs for Time-Varying Signals Using Visibility Graphs
Bozkurt, E., & Ortega, A. (2022) · 2022 30th European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909594 · Cited by 1
Metadata
- Authors
- Dong, X., Thanou, D., Rabbat, M., & Frossard, P.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2019-05
- Volume
- 36
- Issue
- 3
- Pages
- 44-63
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2963384510
- Cited by
- 406
- References
- 84
- FWCI
- 27.40
- Type
- journal article
- Language
- en
- Open access
- green
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- Dong, X., Thanou, D., Rabbat, M., & Frossard, P. (2019). Learning Graphs From Data: A Signal Representation Perspective. IEEE Signal Processing Magazine. https://doi.org/10.1109/msp.2018.2887284
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