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

Part 4: Graphing Like a Pro

In this part, we explore what graph projections are and how they can be used. This will give you the rudimentary knowledge you need to extract projections to perform analysis. These projections can then be extracted for further statistical and analytical uses. You will learn all about how to do this in this chapter. In addition, you will also get an understanding of how to export your projects in igraph and Neo4j. Buckle up and let the analysis flow with the use case looking at analyzing movies with relationships such as acted in and starred with.

Finally, the section on common errors and debugging jumps into the types of errors we have encountered in our careers, and how you can easily get around and resolve these errors and bugs effectively. Let’s think of this as your reference whenever anything untoward occurs in your code.

This part has the following chapters: