In this chapter, we are going to talk about node importance, also known as centrality algorithms. As you will discover, several techniques have been developed, based on the definition of importance for a given graph and a given problem. We will learn about the most famous techniques, starting with degree centrality and the PageRank algorithm used by Google. For the latter, we will go through an example implementation and run it on a simple graph to fully understand how it works and when it can be used. After discovering the other types of centrality algorithms, such as betweenness centrality, we will conclude this chapter with explanations of how centrality algorithms can be used in the context of fraud detection. In this example, we will use, for the first time, the tools provided in the GDS to create a projected graph from Cypher in order to create fake relationships...
Hands-On Graph Analytics with Neo4j
By :
Hands-On Graph Analytics with Neo4j
By:
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)
Preface
Section 1: Graph Modeling with Neo4j
Free Chapter
Graph Databases
The Cypher Query Language
Empowering Your Business with Pure Cypher
Section 2: Graph Algorithms
The Graph Data Science Library and Path Finding
Spatial Data
Node Importance
Community Detection and Similarity Measures
Section 3: Machine Learning on Graphs
Using Graph-based Features in Machine Learning
Predicting Relationships
Graph Embedding - from Graphs to Matrices
Section 4: Neo4j for Production
Using Neo4j in Your Web Application
Neo4j at Scale
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