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

Data Model Transformation – Relational to Graph Databases

Up until this point, we have been getting you ready to work with graph data structures in a real-world environment. This will transition your knowledge even further, taking you from setting up your own MySQL database instances to building a recommendation system. This is an important step forward since many solutions out there in the wild are based on data being extracted from relational database environments, such as MySQL.

In this chapter, we will start with setting up your MySQL graph database and then move on to how to work with graph data and querying the database engine. Carrying on from there, we will look at path-based methods for carrying out your analysis. This will be followed by considerations of schema design for graph solutions and building a recommendation solution so that you can use a user’s gaming history on the popular platform Steam, along with a graph to predict, or highlight, games that...