Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance.
Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline.
By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.

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Spis treści

Graph Data Science with Neo4j. Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project eBook -- spis treści

• 1. Introducing and Installing Neo4j
• 2. Using Existing Data to Build a Knowledge Graph
• 3. Characterizing a Graph Dataset
• 4. Using Graph Algorithms to Characterize a Graph Dataset
• 5. Visualizing Graph Data
• 6. Building a Machine Learning Model with Graph Features
• 7. Automatically Extracting Features with Graph Embeddings for Machine Learning
• 8. Building a GDS Pipeline for Node Classification Model Training
• 9. Predicting Future Edges
• 10. Writing Your Custom Graph Algorithm with the Pregel API