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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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Rai, R., & Sahu, C. K. (2020) · IEEE Access · doi:10.1109/access.2020.2987324 · Cited by 349
Utilizing graph machine learning within drug discovery and development
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., Hayter, J. B. R., Vickers, R., Roberts, C., Tang, J., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab159 · Cited by 313
Self-citationASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Ranjan, E., Sanyal, S., & Talukdar, P. P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5997 · Cited by 26
Fast Interactive Object Annotation With Curve-GCN
Ling, H., Gao, J., Kar, A., Chen, W., & Fidler, S. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00540 · Cited by 317
Principal Neighbourhood Aggregation for Graph NetsPreprint
Corso, G., Cavalleri, L., Beaini, D., Lió, P., & Veličković, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.05718 · Cited by 412
A gentle introduction to deep learning for graphs
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ConvPoint: Continuous convolutions for point cloud processing
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Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
Litany, O., Remez, T., Rodolà, E., Bronstein, A. M., & Bronstein, M. M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.603 · Cited by 298
Self-citationGenerating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning
Gidaris, S., & Komodakis, N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00011 · Cited by 302
Integrating Physics-Based Modeling with Machine Learning: A Survey
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Computing Graph Neural Networks: A Survey from Algorithms to Accelerators
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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