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

Putting the projection to work

We have two projections to put to use now, one read from Neo4j and converted to a Python igraph and one stored more permanently in Neo4j, alongside the original graph data. It’s time to generate some insights about the films and actors in our knowledge graph, drawing on our projections. Now that our projections have a nice, simple, and clean schema to work with, our analysis can be more powerful than if we approached the original knowledge graph data directly. Let’s begin by returning to Python and our co-star graph.

Analyzing the igraph actor projection

As a reminder, we used Python and the Neo4j API to query our knowledge graph using Cypher and return actors who starred alongside each other in the same film. We then converted our results to an edgelist and imported this into igraph, ready for graph analytics. The analysis steps are as follows:

  1. Let’s start with the basics and learn about some of the properties of our...