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

Common Errors and Debugging

In this chapter, we will be working through common errors that you will be presented with as a newly qualified graph data scientist or engineer (which you will be when you have finished reading this book). The focus will be on how to debug graph issues. This will lead on to common issues and how to get around them when working in igraph. Following on from this, we will look at common Neo4j issues, as we have been working igraph, Python, and Neo4j for a large proportion of this book; therefore, it is useful to know how to get around graph database issues as well. We will also touch on how to modify your igraph and Neo4j scripts to achieve the best performance.

There are many examples under each of these sections, with use cases of how and why these issues may present themselves. Therefore, you will be perfectly prepared, after reading this chapter, to debug and solve these common issues in your projects. In the upcoming sections, you will delve into common...