Table of Contents
Preface
Part 1: Introduction to Graph Learning
1
Getting Started with Graph Learning
Why graphs?
Why graph learning?
Why graph neural networks?
Summary
Further reading
2
Graph Theory for Graph Neural Networks
Technical requirements
Introducing graph properties
Directed graphs
Weighted graphs
Connected graphs
Types of graphs
Discovering graph concepts
Fundamental objects
Graph measures
Adjacency matrix representation
Exploring graph algorithms
Breadth-first search
Depth-first search
Summary
3
Creating Node Representations with DeepWalk
Technical requirements
Introducing Word2Vec
CBOW versus skip-gram
Creating skip-grams
The skip-gram model
DeepWalk and random walks
Implementing DeepWalk
Summary
Further reading
Part 2: Fundamentals ...
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