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


In this chapter, we looked at many of the concepts you need to learn when working with graph data models. We started off by looking at making the transition from tabular data files to building nodes, attributes, edges, and edge lists. From there, we then delved into considerations for designing a schema, focusing on a common type of graph in social networks—an undirected heterogeneous graph.

This stood us in good stead for then implementing the model in Python, which focused on the following key methods of building graphs with igraph. First, we looked at adding nodes and attributes to your graph—here, we started with the creation of nodes, then we added attributes for these nodes. Nodes in a graph can be thought of as properties in other object-oriented languages. Next, we looked at the creation of edges to connect your nodes or relationships to the nodes, and we discussed what is meant by an edgelist—a list of relationships (edges) describing connectivity...