Book Image

Graph Data Modeling in Python

By : Gary Hutson, Matt Jackson
Book Image

Graph Data Modeling in Python

By: Gary Hutson, Matt Jackson

Overview of this book

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you’ll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you’ll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you’ll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you’ll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you’ll be able to transform tabular data into powerful graph data models. In essence, you’ll build your knowledge from beginner to advanced-level practitioner in no time.
Table of Contents (16 chapters)
Part 1: Getting Started with Graph Data Modeling
Part 2: Making the Graph Transition
Part 3: Storing and Productionizing Graphs
Part 4: Graphing Like a Pro

Putting the changes into development

In this section, let’s model the changes to Twitter over time by creating our initial schema and refactoring it to meet the new needs of the application, following a planned update. This will include how to set up your database and adding constraints to the database and why and how you should use them. Then, we will look at the steps to implement the pre-schema change and the final step, implementing the changes to our schema. The following sections will cover our main use case of how to put our schema changes into production.

Initializing a new database

As in Chapter 5, Working with Graph Databases, and Chapter 6, Pipeline Development, we will start by creating a new Neo4j graph database. The steps to do this are detailed here:

  1. Open Neo4j and choose Add and then Local DBMS. This will create a new local graph database, which we are going to work with in this section.
  2. For this chapter, we will call the new database Refactor...