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Graph Convolutional Networks (Lecture)

Difficulty level

This lecture covers the concepts of the architecture and convolution of traditional convolutional neural networks, the characteristics of graph and graph convolution, and spectral graph convolutional neural networks and how to perform spectral convolution, as well as the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. This lecture is a part of the Deep Learning Course at NYU's Center for Data Science. Prerequisites for this module include: Modules 1 - 5 of this course and an Introduction to Data Science or a Graduate Level Machine Learning course.

Topics covered in this lesson


0:00:50 – Architecture of Traditional ConvNets
0:13:11 – Convolution of Traditional ConvNets
0:25:29 – Spectral Convolution
0:44:30 – Spectral GCNs
1:06:04 – Template Matching, Isotropic GCNs and Benchmarking GNNs
1:33:06 – Anisotropic GCNs and Conclusion

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