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Graph Convolutional Networks (Tutorial)

Difficulty level

This tutuorial covers the concept of graph convolutional networks and 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


00:00 – Welcome to class
00:20 – Recap from lecture 10 → Graph Transformer Networks (GTNs)
02:50 – Today plan: tensors/representations living on vertices and edges
03:20 – Self-learning resources with Xavier Bresson and Jure Leskovec
04:42 – Graph Convolutional Networks (GCNs)
23:38 – Connection with Convolutional Nets (CNNs) on grids
26:36 – Residual gated GCNs
37:26 – Domain sparsity note
38:27 – PyTorch implementation using Deep Graph Library (DGL)
56:44 – And that was it!

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