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

Debugging graph issues

With graph data models and analysis being more readily employed in industry, and graph database solutions and software gaining popularity, it is likely that you will be exposed to data problems that can be solved in a graphical way. At the same time, graph solutions are still fairly new to many businesses, and you will almost certainly run into errors and issues as a graph data practitioner.

Here, we will cover some of the most common problems you are likely to encounter when using the igraph Python library, as well as some standard errors and issues you may see when using Neo4j. Each issue will be demonstrated with a short example, as an aid in debugging your own issues.

Each error or problem will be shown alongside a real example you can follow along with and resolve. We will need some data with which to demonstrate some issues you may encounter when using Neo4j and Cypher.

For this chapter, our examples will use social network data collected from...