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Geometric Deep Learning: Going beyond Euclidean data
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Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains, such as graphs and manifolds. The purpose of this article is to overview different examples of geometric deep-learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field.
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“In summary, existing surveys only include some of the GNNs and examine a limited number of works, thereby missing the most recent development of GNNs.”
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Metadata
- Authors
- Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P.
- Affiliations
- Tel Aviv University (IL)
- Courant Institute of Mathematical Sciences (US)
- Meta (United Kingdom) (GB)
- Meta (United States) (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- IEEE Signal Processing Magazine
- Published
- 2017-07
- Volume
- 34
- Issue
- 4
- Pages
- 18-42
- ISSN
- 1053-5888 (ISSN-L)
- 1558-0792
- OpenAlex
- W2558748708
- Cited by
- 3,661
- References
- 137
- Citations by year
- 2026: 350 · 2025: 412 · 2024: 388 · 2023: 428 · 2022: 462 · 2021: 567 · 2020: 493 · 2019: 353 · 2018: 181 · 2017: 25
- FWCI
- 169.40
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Signal Processing Magazine (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- geometric deep learning, non-Euclidean data, graph neural networks, manifold learning, structured data, deep neural network
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