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

Who this book is for

The primary aim of this book is to assist existing Python developers of an intermediate level who may have the ambition of getting into graph data modeling. Or, if you are a database developer or IT professional, then the graph database section may give you insights into how graph databases are different from traditional databases.

In essence, this book is targeted at anyone who loves coding in Python and wants to learn more about how to build graph data pipelines, how to ingest and clean data, various ways to store graph data relationships, how to conduct analytical techniques such as community detection and recommendation engine creation, and how to use Cypher to store these relationships and then query the in-memory graph with Cypher.