Graph-Powered Analytics and Machine Learning with TigerGraph (10th Early Release)

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 eeers, architects, and business analysts how to get started with a graph database using rGraph, 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, Xinyu Chan, and Gaurav Deshpande from rGraph present real use cases covering several contemporary business needs. By diving into hands-on exercises using rGraph 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- 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