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


This chapter has taken you on a rollercoaster ride through how to develop knowledge graphs with the powerful igraph package. Firstly, we delved into the data preparation phases of knowledge graph construction, by looking at separating each abstract and then saving this into a separate cleaned abstract file.

Moving on, we looked at the steps needed to design the graph schema in the right way. This involved using popular NLP libraries such as spacy, plus a package we downloaded, and pip installed, the scispacy library for biomedical NLP tasks. Following this, we looked at extracting terms from our dataset and setting bounds on the frequencies of entities to include or exclude.

Once we had the foundations in place, we swiftly moved on to constructing a knowledge graph, from the ground up. This involved performing many of the key data modeling tasks we have been looking at in the chapters up until now. Furthermore, we made sure the graph contained the abstracts and terms...