Graph Databases

Chapter 1. Introduction

Although much of this book talks about graph data models, it is not a book about graph theory.^[2] We don’t need much theory to take advantage of graph databases: provided we understand what a graph is, we’re practically there. With that in mind, let’s refresh our memories about graphs in general.

What Is a Graph?

Formally, a graph is just a collection of vertices and edges—or, in less intimidating language, a set of nodes and the relationships that connect them. Graphs represent entities as nodes and the ways in which those entities relate to the world as relationships. This general-purpose, expressive structure allows us to model all kinds of scenarios, from the construction of a space rocket, to a system of roads, and from the supply-chain or provenance of foodstuff, to medical history for populations, and beyond.

Graphs Are Everywhere

Graphs are extremely useful in understanding a wide diversity of datasets in fields such as science, government, and business. The real world—unlike the forms-based model behind the relational database—is rich and interrelated: uniform and rule-bound in parts, exceptional and irregular in others. Once we understand graphs, we begin to see them in all sorts of places. Gartner, for example, identifies five graphs in the world of business—social, intent, consumption, interest, and mobile—and says that the ability to leverage these graphs provides a “sustainable competitive advantage.”

For example, Twitter’s data is easily represented ...