Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1 – Creating Graph Data in Neo4j
Part 1 – Creating Graph Data in Neo4j
Free Chapter
2
Chapter 1: Introducing and Installing Neo4j
Chapter 1: Introducing and Installing Neo4j
Technical requirements
What is a graph database?
Finding or creating a graph database
Neo4j in the graph databases landscape
Setting up Neo4j
Inserting data into Neo4j with Cypher, the Neo4j query language
Extracting data from Neo4j with Cypher pattern matching
Summary
Further reading
Exercises
3
Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph
Chapter 2: Importing Data into Neo4j to Build a Knowledge Graph
Technical requirements
Importing CSV data into Neo4j with Cypher
Introducing the APOC library to deal with JSON data
Discovering the Wikidata public knowledge graph
Enriching our graph with Wikidata information
Dealing with spatial data in Neo4j
Importing data in the cloud
Summary
Further reading
Exercises
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
Part 2 – Exploring and Characterizing Graph Data with Neo4j
5
Chapter 3: Characterizing a Graph Dataset
Chapter 3: Characterizing a Graph Dataset
Technical requirements
Characterizing a graph from its node and edge properties
Computing the graph degree distribution
Installing and using the Neo4j Python driver
Learning about other characterizing metrics
Summary
Further reading
Exercises
6
Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset
Chapter 4: Using Graph Algorithms to Characterize a Graph Dataset
Technical requirements
Digging into the Neo4j GDS library
Projecting a graph for use by GDS
Computing a node’s degree with GDS
Understanding a graph’s structure by looking for communities
Summary
Further reading
7
Chapter 5: Visualizing Graph Data
Chapter 5: Visualizing Graph Data
Technical requirements
The complexity of graph data visualization
Visualizing a small graph with networkx and matplotlib
Discovering the Neo4j Bloom graph application
Visualizing large graphs with Gephi
Summary
Further reading
Exercises
8
Part 3 – Making Predictions on a Graph
Part 3 – Making Predictions on a Graph
9
Chapter 6: Building a Machine Learning Model with Graph Features
Chapter 6: Building a Machine Learning Model with Graph Features
Technical requirements
Introducing the GDS Python client
Running GDS algorithms from Python and extracting data in a dataframe
Using features from graph algorithms in a scikit-learn pipeline
Summary
Further reading
Exercise
10
Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning
Chapter 7: Automatically Extracting Features with Graph Embeddings for Machine Learning
Technical requirements
Introducing graph embedding algorithms
Using a transductive graph embedding algorithm
Training an inductive embedding algorithm
Computing new node representations
Summary
Further reading
Exercises
11
Chapter 8: Building a GDS Pipeline for Node Classification Model Training
Chapter 8: Building a GDS Pipeline for Node Classification Model Training
Technical requirements
The GDS pipelines
Building and training a pipeline
Making predictions
Using embedding features
Summary
Further reading
Exercise
12
Chapter 9: Predicting Future Edges
Chapter 9: Predicting Future Edges
Technical requirements
Introducing the LP problem
LP features
Building an LP pipeline with the GDS
Summary
Further reading
13
Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java
Chapter 10: Writing Your Custom Graph Algorithms with the Pregel API in Java
Technical requirements
Introducing the Pregel API
Implementing the PageRank algorithm
Testing our code
Using our algorithm from Cypher
Summary
Further reading
Exercises
14
Index
Index
Why subscribe?
15
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Using features from graph algorithms in a scikit-learn pipeline

Now we have all the necessary knowledge to actually use graphs for ML. In this section, we are going to wrap everything up using the GDS Python client to create features and extract data into a dataframe that can be fed into a scikit-learn model training pipeline.

But before we get to this, let me give you an overview of the ML possibilities with graphs.

Machine learning tasks with graphs

In general, ML comprises several types of tasks on various kinds of objects: from sales predictions with time series analysis to patient diagnosis thanks to medical imagery to text translation in many languages with natural language processing (NLP), ML has proven its usefulness in many situations.

In each of these cases, you have to build a dataset made of observations (usually, the rows). Each observation has a certain number of characteristics or features (that is, the columns of your dataset). Depending on the task, you...

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