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

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In Chapter 2, Working with Graph Data Models, we showed how tabular node and edge data can be used to model and construct a graph with Python. We can use this graph to ask questions that would be difficult and inefficient using the original tabular data, thus demonstrating the power of a graph model.

In this chapter, we will be looking more closely, with examples, at the issues that arise when answering graph-like questions using a relational database. In Chapter 1, Introducing Graphs in the Real World, we touched on how path-based operations are inefficient when using tabular data, due to the requirement for repeated table joins.

However, in real situations, data is often not in the form of node properties and edge lists. A huge amount of data, across every sector, is stored in the form of relational data tables. Relational data is often stored and accessed using SQL, or a SQL-like storage system and query language (for example, MySQL).

In this...