Machine learning on graphs | Vidéo | Carmin.tv

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Machine learning on graphs

By Pierre Vandergheynst

Appears in collection : Theoretical Computer Science Spring School: Machine Learning / Ecole de Printemps d'Informatique Théorique : Apprentissage Automatique

There are a plethora of interesting applications that can leverage graph structured data, from drug discovery to route planning, and it is only natural that graph Machine Learning has attracted a lot of attention lately. We will review approaches in graph representation learning, leveraging intuition from graph signal processing to design and study graph neural networks and some of their recent extensions.

Information about the video

Date of recording 23/05/2022
Date of publication 21/06/2022
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers, Graduate Students
Director(s) Jean Petit
Format MP4

Citation data

DOI 10.24350/CIRM.V.19921603
Cite this video Vandergheynst, Pierre (23/05/2022). Machine learning on graphs. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19921603
URL https://dx.doi.org/10.24350/CIRM.V.19921603

Domain(s)

Computation and Language

Bibliography

HAMILTON, William L., YING, Rex, et LESKOVEC, Jure. Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584, 2017. - https://doi.org/10.48550/arXiv.1709.05584
CHUNG, Fan RK et GRAHAM, Fan Chung. Spectral graph theory. American Mathematical Soc., 1997. - https://bookstore.ams.org/cbms-92
BRONSTEIN, Michael M., BRUNA, Joan, LECUN, Yann, et al. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 2017, vol. 34, no 4, p. 18-42. - https://doi.org/10.1109/MSP.2017.2693418

MSC codes

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