Graph Data Science with Neo4j EPUB: Learn Graph ML from Scratch

Graph data is exploding across industries—from detecting fraud to powering recommendation systems—and Neo4j is at the forefront of this revolution. If you’ve been searching for the “Graph Data Science with Neo4j EPUB”, you’re not alone. Developers, data scientists, and architects want portable, readable versions of cutting-edge graph resources to keep learning on the go.

In this article, we’ll explore what the book offers, how to find a legal EPUB version, and how to begin using graph data science in Neo4j, complete with code snippets and use cases.

What Is “Graph Data Science with Neo4j”?

This book serves as a comprehensive guide to analyzing, modeling, and solving data science problems using graph structures. Written with both developers and data scientists in mind, the book covers:

• Introduction to Graph Theory and Neo4j
• Cypher Query Language
• Graph Algorithms (PageRank, Community Detection, etc.)
• Embedding Models and Machine Learning with GDS
• Use cases: Recommendations, fraud, churn, logistics

Where to Get the EPUB Version

While the EPUB version is often sought for portability, it’s important to download it legally to support the authors and publishers.

Key Concepts from the Book with Code Examples

CALL gds.graph.project(
  'user-product-graph',
  ['User', 'Product'],
  {
    BOUGHT: {
      type: 'BOUGHT',
      orientation: 'UNDIRECTED'
    }
  }
);

CALL gds.pageRank.stream('user-product-graph')

YIELD nodeId, score

RETURN gds.util.asNode(nodeId).name AS name, score

ORDER BY score DESC

LIMIT 10;

MATCH (u:User)-[:BOUGHT]->(p:Product)<-[:BOUGHT]-(other:User)
WHERE u.name = 'Alice' AND u <> other
MATCH (other)-[:BOUGHT]->(rec:Product)
WHERE NOT (u)-[:BOUGHT]->(rec)
RETURN rec.name, count(*) AS freq
ORDER BY freq DESC;

Why Use Graph Data Science?

Graph-based machine learning offers massive advantages over tabular ML:

• Better context preservation
• Explainability with graph paths
• Real-time analytics with sub-second query responses
• Superior results in fraud, supply chain, social networks, etc.

Pro Tip: Neo4j GDS Library

Make sure you have the Graph Data Science plugin installed in your Neo4j instance:

docker run \
  --name neo4j-gds \
  -p7474:7474 -p7687:7687 \
  -e NEO4J_AUTH=neo4j/test \
  -e NEO4JLABS_PLUGINS='["graph-data-science"]' \
  -e NEO4J_dbms_memory_heap_initial__size=1G \
  -e NEO4J_dbms_memory_heap_max__size=2G \
  neo4j:5.9

Conclusion

If you’re diving into Graph Data Science with Neo4j, the EPUB version is a great way to learn on-the-go. But beyond reading, try the code, build prototypes, and leverage the GDS library to apply what you learn.

Whether you’re a startup trying to detect fraud or an enterprise optimizing delivery routes, Neo4j’s graph algorithms can be game-changing.

Need implementation help? Reach out to our experts—we turn theory into production-ready graph solutions.