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


This was a pivotal chapter if you want to really expand and extend the skill sets you have learned throughout this book and start putting them into practice. Prior to this chapter, we focused on graph pipelines. This extends the knowledge and insights you have gained further by looking at the importance of having a flexible and responsive schema to change.

Stemming from the tools you have been equipped with for schema change, we looked at what this means for your existing schema and what you need to know to build an adaptive schema. Furthermore, we looked at the reason for refactoring schemas, focusing on why you may need to refactor such as because of graph database relationships and the various changes that need to be considered when building your initial schema design.

Following the initial concepts of schema design, we then moved on to looking at how to effectively evolve your schema in production.

This focused on a use case of adding additional Twitter Circles...