Databases Reference
In-Depth Information
CHAPTER 1
Introduction
Although much of this topic talks about graph data models, it is not a topic about graph
theory.
1
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 lan‐
guage, 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
1. For introductions to graph theory, see Richard J. Trudeau,
Introduction To Graph Theory
(Dover, 1993) and
Gary Chartrand,
Introductory Graph Theory
(Dover, 1985). For an excellent introduction to how graphs
provide insight into complex events and behaviors, see David Easley and Jon Kleinberg,
Networks, Crowds,
and Markets: Reasoning about a Highly Connected World
(Cambridge University Press, 2010).
