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Graph SAGE - Inductive Representation Learning on Large Graphs - GNN Paper Explained

Aleksa Gordić - The AI Epiphany via YouTube

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Overview

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This course provides a detailed explanation of the GraphSAGE paper and its inductive method for learning node representations on large graphs. It covers neighborhood sampling, aggregation functions, training, expressiveness, mini-batch learning, and comparisons with GCN and GAT.

Syllabus

Intro
Problems with previous methods
High-level overview of the method
Some notes on the related work
Pseudo-code explanation
How do we train Graph SAGE?
Note on the neighborhood function
Aggregator functions
Results
Expressiveness of Graph SAGE
Mini-batch version
Problems with graph embedding methods drift
Comparison with GCN and GAT

Taught by

Aleksa Gordić - The AI Epiphany

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