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

Making product recommendations

Often, when using an online retail application or service, you will find that products are recommended to you. This can be driven by browser cookies, products that you have viewed already, or previous purchases. In our case, we have information on previous product purchases, so we will use that data to make suggestions.

Even with the choice of using prior purchases to inform our recommendations, the decision to recommend a specific product to you can be made in many different ways. If a customer has purchased an item from a specific brand, would they be interested in owning more products from the same brand? Or, should we recommend a product to a customer based on other customers behavior?

In the following sections, we will design and test a few different recommendation methods, and this starts in the next section by looking at product recommendations by brand.

Product recommendations by brand

As we briefly discussed in the Schema design section...