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


In this chapter, we compared and contrasted traditional relational databases and graph databases to perform path-based analyses. Our path-based analysis of choice was recommending a game to a user on the Steam publishing platform and was performed in both MySQL and igraph.

MySQL, and other relational databases, can be used to find paths between tables of related entities, such as users and the games they play and purchase. However, this involves performing self-joins on the same table or repeatedly querying the same table. On the other hand, graph databases and data models are natively set up for path-based queries, so we used igraph to recommend a game to a user based on paths between users and games in our graph.

Then, we covered how to move data over from MySQL to Python igraph, both step-by-step and with a generic set of methods that can be used for any directed, heterogeneous graph.

Finally, we set up a more sophisticated system to make game recommendations to...