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)
1
Part 1: Getting Started with Graph Data Modeling
4
Part 2: Making the Graph Transition
7
Part 3: Storing and Productionizing Graphs
11
Part 4: Graphing Like a Pro

Preface

Organizations across the globe are starting to use graph approaches and visualization techniques to make sense of complex networks. These networks are present in many industries, ranging from social network analysis (analyzing the connections of people interacting on social networks) to fraud detection (looking at transactions in a network to spot outliers), modeling the stability of systems such as rail and energy grids, and as critical components of recommendation engines that are used in many of your favorite online streaming services, for example, Netflix, Prime, and so on.

This book provides you with the tools to get up and running with these methods while working with a familiar language, such as Python. We start by looking at how you can create graphs in igraph NetworkX and how these can be used to carry out sophisticated graph analytics. We will then delve into the world of Neo4j and graph databases, as well as equipping you with the knowledge to query graph databases with the Cypher query language.