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Hands-On Graph Analytics with Neo4j

Hands-On Graph Analytics with Neo4j

By : Scifo
4.6 (9)
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Hands-On Graph Analytics with Neo4j

Hands-On Graph Analytics with Neo4j

4.6 (9)
By: Scifo

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1: Graph Modeling with Neo4j
5
Section 2: Graph Algorithms
10
Section 3: Machine Learning on Graphs
14
Section 4: Neo4j for Production

Summary

This chapter gave an overview of classical data science pipelines and how to integrate graph data into them. Thanks to the Neo4j Python driver, you are now able to import Neo4j data into a pandas DataFrame, which can then be used as usual in any other applications, such as model training with scikit-learn. You have also learned how to programmatically run a graph algorithm from the GDS and use the result as a new type of feature for your model.

In the following chapters, we will continue our journey through graph analytics. In this chapter, we stuck to classical machine learning methods such as decision trees. We will now go on to learn how the graph structure can be used to answer different kinds of questions, starting with the link prediction problem, which we are going to tackle in the next chapter.

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