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


In this chapter, we have used both Python and Neo4j to create, store, and analyze graph projections, demonstrating their power in both the efficiency and interpretability of results. Each technology has its own separate strengths. In Neo4j, we have less readily available access to complex graph data science algorithms to analyze our projection, compared to what we can easily carry out in Python with igraph.

However, using Neo4j is a more permanent storage option and suitable for a projection we might want to repeatedly read and write to. For any given use case, it is important to consider what the most appropriate projection creation and storage tool is for the task at hand.

These skills you have acquired will allow you to navigate between Neo4j, Python, and igraph with ease and will have set a strong foundation to build pipelines between the two technologies – a happy marriage indeed.

In the next, and final, chapter, you will learn about some of the common...