The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Graph Signal Processing: Overview, Challenges, and Applications
Open accessAbstract
Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.
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Citations
Cites
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The economy of brain network organization
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Dynamic reconfiguration of human brain networks during learning
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Discrete Signal Processing on Graphs
Sandryhaila, A., & Moura, J. M. F. (2013) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2238935 · Cited by 1,531
Self-citationGeodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
Self-citationHuman brain networks function in connectome-specific harmonic waves
Atasoy, S., Donnelly, I., & Pearson, J. (2016) · Nature Communications · doi:10.1038/ncomms10340 · Cited by 381 · cited 1×
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Vertex-frequency analysis on graphs
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Cited by
Deep Learning on Graphs: A Survey
Zhang, Z., Cui, P., & Zhu, W. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2981333 · Cited by 1,633
What are higher-order networks?
Bick, C., Gross, E., Harrington, H. A., & Schaub, M. T. (2023) · SIAM Review · doi:10.1137/21m1414024 · Cited by 420
Learning Graphs From Data: A Signal Representation Perspective
Dong, X., Thanou, D., Rabbat, M., & Frossard, P. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2887284 · Cited by 406
Self-citationConnecting the Dots: Identifying Network Structure via Graph Signal Processing
Mateos, G., Segarra, S., Marques, A. G., & Ribeiro, A. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2890143 · Cited by 369
Understanding Physics-Informed Neural Networks: Techniques, Applications, Trends, and Challenges
Farea, A., Yli-Harja, O., & Emmert-Streib, F. (2024) · AI · doi:10.3390/ai5030074 · Cited by 259
Metadata
- Authors
- Ortega, A., Frossard, P., Kovačević, J., Moura, J. M. F., & Vandergheynst, P.
- Venue
- Proceedings of the IEEE
- Published
- 2018-05
- Volume
- 106
- Issue
- 5
- Pages
- 808-828
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2796431263
- Cited by
- 1,787
- References
- 9
- FWCI
- 95.90
- Type
- journal article
- Language
- en
- Open access
- green
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- Ortega, A., Frossard, P., Kovačević, J., Moura, J. M. F., & Vandergheynst, P. (2018). Graph Signal Processing: Overview, Challenges, and Applications. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2018.2820126
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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