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

From relational to graph databases

Due to the poor performance of our relational database in answering graph-like questions, we may want to move our tabular data into a graph format.

First, we will consider a sensible graph schema for our data, based on the information we have available, before writing a pipeline to move data from MySQL into a Python igraph network. By doing this, we can benchmark how a graphical approach to our path-based question performs, in comparison to the same question we answered with SQL.

Schema design

In our tables, we have two types of entities, users and games, which have different properties. Because of this, it is wise to consider users and games as different node types.

For users, we only have a unique ID for each user. To add data to an igraph graph, we will need to add an increasing integer igraph node ID for each distinct node, as we learned in Chapter 1, Introducing Graphs in the Real World, and Chapter 2, Working with Graph Data Models...