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


We started this chapter by looking at design considerations for a graph database pipeline, and we also refamiliarized ourselves with how to set up a Neo4j graph database. Our use case for this chapter was creating a graph database for retail, and we designed a schema and pipeline. With our schema considerations mapped out and considered, we then looked at how you can add static data and introduced fake data to simulate customer interactions. Obviously, this would not be fake in practice but served as a good way to test out if our desired schema functioned the way we would want it to in a production environment.

The ultimate aim of this chapter was to set up a schema that would enable us to make product recommendations based on similar products customers buy. The first step was to get refamiliarized with Cypher (Neo4j’s query language – similar to SQL) and Python for working with this data, followed by making recommendations by brand. This then led on to recommendations...