Deep Learning - Graph Deep Learning Part 2

In this video, we demonstrate how to go from spectral to spatial domain in graphs.

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Further Reading:
A gentle Introduction to Deep Learning

References

[1]: Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016).
[2]: Hamilton, Will, Zhitao Ying, and Jure Leskovec. "Inductive representation learning on large graphs." Advances in neural information processing systems. 2017.
[3]: Wolterink, Jelmer M., Tim Leiner, and Ivana Išgum. "Graph convolutional networks for coronary artery segmentation in cardiac CT angiography." International Workshop on Graph Learning in Medical Imaging. Springer, Cham, 2019.
[4]: Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." arXiv preprint arXiv:1901.00596 (2019).
[5]: Bronstein, Michael et al. Lecture “Geometric deep learning on graphs and manifolds” held at SIAM Tutorial Portlan (2018)

Image References

[a] https://de.serlo.org/mathe/funktionen/funktionsbegriff/funktionen-graphen/graph-funktion
[b] https://www.nwrfc.noaa.gov/snow/plot_SWE.php?id=AFSW1
[c] https://tennisbeiolympia.wordpress.com/meilensteine/steffi-graf/
[d] https://www.pinterest.de/pin/624381935818627852/
[e] https://www.uihere.com/free-cliparts/the-pentagon-pentagram-symbol-regular-polygon-golden-five-pointed-star-2282605
[f] http://geometricdeeplearning.com/ (Geometric Deep Learning on Graphs and Manifolds)
[g] https://i.stack.imgur.com/NU7y2.png
[h] https://de.wikipedia.org/wiki/Datei:Convolution_Animation_(Gaussian).gif
[i]https://www.researchgate.net/publication/306293638/figure/fig1/AS:396934507450372@1471647969381/Example-of-centerline- extracted-left-and-coronary-artery-tree-mesh-reconstruction.png
[j] https://www.eurorad.org/sites/default/files/styles/figure_image_teaser_large/public/figure_image/2018-08/0000015888/000006.jpg?itok=hwX1sbCO