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Introduction to Graph Neural Networks (self-paced)
Theory
Neural Message Passing (21:45)
Permutation Symmetries (24:43)
Graph Isomorphism (16:36)
How Powerful are Graph Neural Networks? (24:33)
The Graph Laplacian (33:40)
Fourier Transforms, Wavelets and Convolutions on Graphs (31:14)
The Math of Graph Convolutional Networks (15:51)
Getting started with Hands-On Section
Welcome and Introduction
Hands-On Segment 1: Node Classification
Overview of this Section
Node Classification Overview (7:54)
Tools of the Trade (17:16)
Transductive vs Inductive (14:59)
Node Classification Solution Walk-Thru (17:37)
Hands-On Segment 2: Link Prediction
Overview of this Section
Link Prediction Overview (18:21)
Over-squashing and Over-smoothing (10:10)
Link Prediction Solution Walk-Thru (12:42)
Hands-On Segment 3: Graph Classification
Overview of this Section
Graph Classification Overview (10:51)
Improving Scalability (9:03)
Graph Classification Solution Walk-Thru (28:08)
BONUS: Author Interviews
Introduction
How DeepMind learns physics simulators with Graph Networks (35:33)
How Uber uses Graph Neural Networks to recommend you food (54:00)
Equivariant Subgraph Aggregation Networks (70:05)
Scaling Graph Neural Networks to Twitter-scale (56:55)
Knowledge Graphs for Drug Repurposing (74:58)
Welcome and Introduction
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