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

Part 2: Making the Graph Transition

Armed with what we learned from the previous part around the fundamentals, we can now move on to explain how and why graph databases are different to traditional relational database structures, and how and why you would want to use them. We will be working in MySQL and Python in the data model transformation chapter, which will culminate in building a recommendation engine to recommend a game to a user.

Once we have that chapter in our arsenal, we will move on to delve into how we can build a knowledge graph. This will involve getting our hands dirty with some data ingestion and cleaning, before we then create our knowledge graph and perform community detection over the top, to find medical abstracts that relate to a specific subject, as community detection’s role is to find similar communities in entities, or in this sense, similar research based on a specific abstract text and what terms are mentioned in the text.

This part has the...