# Introducing Graphs in the Real World

Social network analysis, fraud detection, modeling the stability of systems (for example, rail and energy grids), and recommendation systems all rely on graphs as the lynchpin underpinning these types of networks. In each of these examples, the relationships between individual people, bank accounts, or other single units are fundamental to describe and model the data. As opposed to traditional data models, a graph is a perfect way to represent groups of interacting elements.

This chapter will serve as an introduction to why graphs are important and introduce you to the fundamentals of what makes up a graph network. Moreover, we will look at how to transition from traditional data storage strategies, such as **relational databases** (**RDBs**), to how you can use this knowledge to work with **graph databases** (**GDBs**). Throughout this book, we will be working with a popular graph database, namely Neo4j. This will be followed by an explanation of how graphs are utilized in the *real world* and then a gentle introduction to working with the main package workhorses, known as igraph and NetworkX, which are the best and most stable graph packages for graph data analysis and modeling.

In this chapter, we’re going to cover the following topics:

- Why should you use graphs?
- The fundamentals of nodes and edges and the properties of a graph
- Comparing RDBs and GDBs
- The use of graphs across various industries
- Introduction to NetworkX and igraph