Book Image

Hands-On Graph Analytics with Neo4j

By : Estelle Scifo
Book Image

Hands-On Graph Analytics with Neo4j

By: Estelle 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.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Graph Modeling with Neo4j
Section 1: Graph Modeling with Neo4j
Free Chapter
2
Graph Databases
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
3
The Cypher Query Language
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
4
Empowering Your Business with Pure Cypher
Empowering Your Business with Pure Cypher
Technical requirements
Knowledge graphs
Graph-based search
Recommendation engine
Summary
Questions
Further reading
5
Section 2: Graph Algorithms
Section 2: Graph Algorithms
6
The Graph Data Science Library and Path Finding
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
7
Spatial Data
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
8
Node Importance
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
9
Community Detection and Similarity Measures
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
10
Section 3: Machine Learning on Graphs
Section 3: Machine Learning on Graphs
11
Using Graph-based Features in Machine Learning
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
12
Predicting Relationships
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
13
Graph Embedding - from Graphs to Matrices
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
14
Section 4: Neo4j for Production
Section 4: Neo4j for Production
15
Using Neo4j in Your Web Application
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
16
Neo4j at Scale
Neo4j at Scale
Technical requirements
Measuring GDS performance
Configuring Neo4j 4.0 for big data
Summary
17
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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...

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