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

Pipeline Development

This chapter will involve you, as a progressing graph data scientist, getting directly involved in building production-grade schemas. Here, we will teach you everything we have acquired from our years of experience as graph practitioners.

The use case for our pipeline design in this chapter will be to develop a schema that can be used to look at customers purchasing habits, with the ultimate aim of building a recommendations system that can be used as new (unseen) data is added to the graph. This will function very much like a streaming service, where, instead of You might like this film recommendations, you will be given recommendations on products you are likely to buy. We will look at querying methods looking at product similarity, alongside a popular similarity matching method called Jaccard similarity.

Again, you will be working extensively with Neo4j and Python to integrate and build the pipeline seen in many production environments. I hope you are...