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

What are projections?

Data in graph data models usually comprises relationships between things, whether they be people, languages, transport hubs, or any of the other examples we’ve seen throughout the previous chapters. Plenty of data models have several types of nodes and relationships, making one node or edge not equal in meaning or value to another.

These types of graphs are known as heterogeneous graphs, and we have seen them throughout this book. One type of heterogeneous graph is a bipartite graph, where there are two types of nodes. In bipartite graphs, only different types of nodes can share edges. For example, a citation network might be represented as a bipartite graph, with authors connected to articles they have written.

Analysis of heterogeneous graphs with multiple node and edge types can be tricky. To illustrate this, first consider the authorship graph in Figure 8.1. We can answer some simple questions easily, using this graph’s native structure...