With the rapid rise of graph databases, organizations are now implementing advanced analytics and machine learning solutions to help drive business outcomes. This practical guide shows data scientists, data engineers, architects, and business analysts how to get started with a graph database using TigerGraph, one of the leading graph database models available.

You'll explore a three-stage approach to deriving value from connected data: connect, analyze, and learn. Victor Lee, Phuc Kien Nguyen, and Alexander Thomas present real use cases covering several contemporary business needs. By diving into hands-on exercises using TigerGraph Cloud, you'll quickly become proficient at designing and managing advanced analytics and machine learning solutions for your organization.

• Use graph thinking to connect, analyze, and learn from data for advanced analytics and machine learning
• Learn how graph analytics and machine learning can deliver key business insights and outcomes
• Use five core categories of graph algorithms to drive advanced analytics and machine learning
• Deliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizen
• Discover insights from connected data through machine learning and advanced analytics

Table of Contents

Chapter 1. Connections Are Everything

Connections Change Everything

Graph Analytics and Machine Learning

Chapter Summary

Part I. Connect

Chapter 2. Connect and Explore Data

Chapter 3. See Your Customers and Business Better: 360 Graphs

Chapter 4. Studying Startup Investments

Chapter 5. Detecting Fraud and Money Laundering Patterns

Part II. Analyze

Chapter 6. Analyzing Connections for Deeper Insight

Chapter 7. Better Referrals and Recommendations

Chapter 8. Strengthening Cybersecurity

Chapter 9. Analyzing Airline Flight Routes

Part Ill. Learn

Chapter 10. Graph-Powered Machine Learning Methods

Chapter 11. Entity Resolution Revisited

Chapter 12. Improving Fraud Detection

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 algorithms and machine learning techniques, we present some mathematical equations involving sets, summation, and limits. Those equations, however, are a supplement to our explanations with words and figures.

In the use case chapters, we will be running prewritten GSQL code on the TigerGraph Cloud platform. You’ll just need a computer and internet access. If you are familiar with the SQL database query language and any mainstream programming language, then you will be able to understand much of the GSQL code. If you are not, you can simply follow the instructions and run the prewritten use case examples while following along with the commentary in the book.

About the Author

Victor Lee is Vice President of Machine Learning and AI at TigerGraph. His Ph.D. dissertation was on graph-based similarity and ranking. Dr. Lee has co-authored book chapters on decision trees and dense subgraph discovery. Teaching and training have also been central to his career journey, with activities ranging from developing training materials for chip design to writing the first version of TigerGraph's technical documentation, from teaching 12 years as a full-time or part-time classroom instructor to presenting numerous webinars and in-person workshops.

Phuc Kien Nguyen is a data scientist at ABN Amro Bank in Amsterdam. For the past five years, he has helped develop solutions and machine learning models to combat financial crime. He holds an MSc degree in Information Architecture from Delft University of Technology. Next to his day-to-day job, he writes articles at Medium about data science and network analytics. He has a great passion for storytelling, especially through video games. In his spare time, he loves to play football and catch up with the latest development in technology.