Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning

• Get up and running with graph analytics with the help of real-world examples
• Explore various use cases such as fraud detection, graph-based search, and recommendation systems
• Get to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scaling

Neo4j is a graph database that includes plugins to run complex graph algorithms.

The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j.

By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data.

• Become well-versed with Neo4j graph database building blocks, nodes, and relationships
• Discover how to create, update, and delete nodes and relationships using Cypher querying
• Use graphs to improve web search and recommendations
• Understand graph algorithms such as pathfinding, spatial search, centrality, and community detection
• Find out different steps to integrate graphs in a normal machine learning pipeline
• Formulate a link prediction problem in the context of machine learning
• Implement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphs

This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.