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

Perfect Projections

In this chapter, we are going to focus on creating graph projections, which you will be able to perform sophisticated analysis on, and even use other algorithms, such as machine learning and statistical methods, to form insights. We will start by explaining what projections are, leading on to how to use projections in practice. We’ll draw upon how you can create projections in igraph and Neo4j, using a combination of the Cypher query language and Python.

For our use case, we will focus on popular movies, with a focus on using graph data science to find what films actors have appeared in, co-starred in, and multiple other relationships we can define with flexible graph structures. By the end of this chapter, you will be able to create a projection and put it to work for your use case.

We will be covering the following main topics:

  • What are projections?
  • How to use a projection
  • Putting the projection to work