Graph-Powered Analytics and Machine Learning with TigerGraph

Preface

Objectives

The goal of this book is to introduce you to the concepts, techniques, and tools for graph data structures, graph analytics, and graph machine learning. When you’ve finished the book, we hope you’ll understand how graph analytics can be used to address a range of real-world problems. We want you to be able to answer questions like the following: Is graph a good fit for this task? What tools and techniques should I use? What are the meaningful relationships in my data, and how do I formulate a task in terms of relationship analysis?

In our experience, we see that many people quickly grasp the general concept and structure of graphs, but it takes more effort and experience to “think graph,” that is, to develop the intuition for how best to model your data as a graph and then to formulate an analytical task as a graph query. Each chapter begins with a list of its objectives. The objectives fall into three general areas: learning concepts about graph analytics and machine learning; solving particular problems with graph analytics; and understanding how to use the GSQL query language and the TigerGraph graph platform.

Audience and Prerequisites

We designed this book for anyone who has an interest in data analytics and wants to learn about graph analytics. You don’t need to be a serious programmer or a data scientist, but some exposure to databases and programming concepts will definitely help you to follow the presentations. When we go into depth on a few graph ...