Graph-Powered Analytics and Machine Learning with TigerGraph

Chapter 2. Connect and Explore Data

In Chapter 1, we showed the potential of graph analytics and machine learning applied to human and business endeavors, and we proposed to present the details in three stages: the power of connected data, the power of graph analytics, and the power of graph machine learning. In this chapter, we will take a deep dive into the first stage: the power of connected data.

Before we delve into the power of connected data, we need to lay some groundwork. We start by introducing the concepts and nomenclature of the graph data model. If you are already familiar with graphs, you may want to skim this section to check that we’re on the same page with regard to terminology. Besides graphs themselves, we’ll cover the important concepts of a graph schema and traversing a graph. Traversal is how we search for data and connections in a graph.

And along the way, we talk about the differences between graph and relational databases and how we can ask questions and solve problems with graph analytics that would not be feasible in a relational database.

From that foundational understanding of what a graph is, we move on to present examples of the power of a graph by illustrating six ways that graph data provides you with more insight and more analytical capability than tabular data.

After completing this chapter, you should be able to:

• Use the standard terminology for describing graphs

• Know the difference between a graph schema and a graph instance

• Create a ...