Week 13 – Lecture: Graph Convolutional Networks (GCNs)

0:00:00 – Week 13 – Lecture

LECTURE Part A: http://bit.ly/pDL-en-13-1
In this section, we discuss the architecture and convolution of traditional convolutional neural networks. Then we extend to the graph domain. We understand the characteristics of graph and define the graph convolution. Finally, we introduce spectral graph convolutional neural networks and discuss how to perform spectral convolution.
0:00:50 – Architecture of Traditional ConvNets
0:13:11 – Convolution of Traditional ConvNets
0:25:29 – Spectral Convolution

LECTURE Part B: http://bit.ly/pDL-en-13-2
This section covers 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. Various architectures employing the two approaches are detailed out with their corresponding pros & cons, experiments, benchmarks and applications.
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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