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

Technical requirements

We will be using the Jupyter Notebook to run our coding exercises, which requires python>=3.8.0. In addition, the following packages will need to be installed, with the pip install command, in your environment:

  • igraph==0.9.8
  • spacy==3.4.4
  • scispacy==0.5.1
  • matplotlib

Alternatively, you could run pip install –r requirements.txt, in the supporting requirements file, to install all the supporting dependencies for this chapter.

For this chapter, you will also need to install a text corpus for some Natural Language Processing (NLP). Go to and download the en_core_sci_sm model. In a command prompt or terminal window, navigate to where this is downloaded and run pip install en_core_sci_sm-0.5.1.tar.gz.

All notebooks, with the coding exercises, are available at the following GitHub link: