What this book covers
Chapter 1, Introducing and Installing Neo4j, introduces the basic principles of graph databases and gives instructions on how to set up Neo4j locally, create your first graph, and write your first Cypher queries.
Chapter 2, Using Existing Data to Build a Knowledge Graph, guides you through loading data into Neo4j from different formats (CSV, JSON, and an HTTP API). This is where you will build the dataset that will be used throughout this book.
Chapter 3, Characterizing a Graph Dataset, introduces some key metrics to differentiate one graph dataset from another.
Chapter 4, Using Graph Algorithms to Characterize a Graph Dataset, goes deeper into understanding a graph dataset by using graph algorithms. This is the chapter where you will start to use the Neo4j GDS plugin.
Chapter 5, Visualizing Graph Data, delves into graph data visualization by drawing nodes and edges, starting from static representations and moving on to dynamic ones.
Chapter 6, Building a Machine Learning Model with Graph Features, talks about machine learning model training using scikit-learn. This is where we will first use the GDS Python client.
Chapter 7, Automating Feature Extraction with Graph Embeddings for Machine Learning, introduces the concept of node embedding, with practical examples using the Neo4j GDS library.
Chapter 8, Building a GDS Pipeline for Node Classification Model Training, introduces the topic of node classification within GDS without involving a third-party tool.
Chapter 9, Predicting Future Edges, gives a short introduction to the topic of link prediction, a graph-specific machine learning task.
Chapter 10, Writing Your Custom Graph Algorithms with the Pregel API in Java, covers the exciting topic of building an extension for the GDS plugin.
