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

Making the transition from tabular to graph data

To introduce the power of a graph data model, we will first focus on using a real social media dataset, from Facebook. This open source data contains information on Facebook pages, their name, and the type of page. Four types of pages are included, namely those for TV shows, companies, politicians, and governmental organizations. In addition, we have data on mutual likes between pages. If two pages like each other on Facebook, this is represented in our data.

It is at this stage that we can start to consider how best to model this dataset. To assemble a graph, we know from Chapter 1, Introducing Graphs in the Real World, that we need to have things represented by nodes, and relationships between those nodes represented by edges.

In the upcoming sections, we will look at examining data, thoughts, and considerations when designing efficient and effective schemas, and then we will get on to implementing the model in Python. Let’...