Hands-On Graph Analytics with Neo4j

By : Scifo

4.6 (9)

Buy this Book

Hands-On Graph Analytics with Neo4j

4.6 (9)

By: Scifo

Buy this Book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: Graph Modeling with Neo4j

Free Chapter

Graph Databases

Graph definition and examples

Moving from SQL to graph databases

Neo4j – the nodes, relationships, and properties model

Understanding graph properties

Considerations for graph modeling in Neo4j

Summary

Further reading

The Cypher Query Language

Technical requirements

Creating nodes and relationships

Updating and deleting nodes and relationships

Pattern matching and data retrieval

Using aggregation functions

Importing data from CSV or JSON

Measuring performance and tuning your query for speed

Summary

Questions

Further reading

Empowering Your Business with Pure Cypher

Technical requirements

Knowledge graphs

Graph-based search

Recommendation engine

Summary

Questions

Further reading

Section 2: Graph Algorithms

The Graph Data Science Library and Path Finding

Technical requirements

Introducing the Graph Data Science plugin

Understanding the importance of shortest path algorithms through their applications

Dijkstra's shortest paths algorithm

Finding the shortest path with the A* algorithm and its heuristics

Discovering the other path-related algorithms in the GDS plugin

Optimizing processes using graphs

Summary

Questions

Further reading

Spatial Data

Technical requirements

Representing spatial attributes

Creating a geometry layer in Neo4j with neo4j-spatial

Performing spatial queries

Finding the shortest path based on distance

Visualizing spatial data with Neo4j

Summary

Questions

Further reading

Node Importance

Technical requirements

Defining importance

Computing degree centrality

Understanding the PageRank algorithm

Path-based centrality metrics

Applying centrality to fraud detection

Summary

Exercises

Further reading

Community Detection and Similarity Measures

Technical requirements

Introducing community detection and its applications

Detecting graph components and visualizing communities

Running the Label Propagation algorithm

Understanding the Louvain algorithm

Going beyond Louvain for overlapping community detection

Measuring the similarity between nodes

Summary

Questions

Further reading

Section 3: Machine Learning on Graphs

Using Graph-based Features in Machine Learning

Technical requirements

Building a data science project

The steps toward graph machine learning

Using graph-based features with pandas and scikit-learn

Automating graph-based feature creation with the Neo4j Python driver

Summary

Questions

Further reading

Predicting Relationships

Technical requirements

Why use link prediction?

Creating link prediction metrics with Neo4j

Building a link prediction model using an ROC curve

Summary

Questions

Further reading

Graph Embedding - from Graphs to Matrices

Technical requirements

Why do we need embedding?

Adjacency-based embedding

Extracting embeddings from artificial neural networks

Graph neural networks

Going further with graph algorithms

Summary

Questions

Further reading

Section 4: Neo4j for Production

Using Neo4j in Your Web Application

Technical requirements

Creating a full-stack web application using Python and Graph Object Mappers

Understanding GraphQL APIs by example – GitHub API v4

Developing a React application using GRANDstack

Summary

Questions

Further reading

Neo4j at Scale

Technical requirements

Measuring GDS performance

Configuring Neo4j 4.0 for big data

Summary

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Introducing community detection and its applications

Community detection gathers techniques that have been developed to understand the structure of a graph and extract information from it. This structure can then be used in many applications, such as recommendation engines, fraud detection, property prediction, and link prediction.

Throughout this chapter, I will use the words community, cluster, or partition to refer to a group of nodes sharing common properties.

Identifying clusters of nodes

The following image shows the graph of Neo4j GitHub users we built in Chapter 2, The Cypher Query Language. Community detection algorithms were able to identify several communities:

Image generated using the Louvain algorithm and neoviz.js

By the end of this chapter, you will be able to reproduce this image. Further analysis will be needed to understand the common properties of the users belonging to the violet community. A deeper analysis of this graph teaches us that the users in the violet community...