Book Overview & Buying
Table Of Contents
Feedback & Rating

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

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

4.6 (9)

5 star

66.7%

4 star

22.2%

3 star

11.1%

2 star

1 star

Finding the shortest path based on distance

Spatial data and path finding algorithms are very much related. In this section, we are going to use a dataset representing the road network in New York, neo4j-spatial, and the GDS plugin (see Chapter 4, The Graph Data Science Library and Path Finding) to build a routing system.

The specifications for this routing application are the following:

The user will input the start and end locations as (latitude, longitude) tuples.
The system must return an ordered list of the streets the user needs to follow in order to go from his start to his end location by traveling the shortest distance.

Let's start by discovering and preparing the data.

Importing the data

In order to build a routing engine, we need a precise description of the road network in our area of interest. Luckily, the street network of New York is available as open data. You can find this file in the GitHub repository for this book, together with more information about its provenance...