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

Graph pipeline development

In the last chapter, we learned how to interface Python with a Neo4j database. We harnessed Neo4j’s long-term graph storage solution to set up a more realistic, production-like system, which could be queried to find air travel routes, according to several parameters.

We set the graph database up by writing a large amount of data to it using static queries. At the point of setting up the database, we knew what data we wanted it to hold, and we wrote Cypher queries to get our data in, in bulk. The focus of the resulting graph was on delivering read query results to, for example, a frontend web application. These read queries were executed against the database only when we had a question to ask of the data.

However, in reality, these graph database systems often serve as the backend to applications that frequently and automatically send queries to a graph database. These queries may not only be driven by direct user behavior but also executed to...