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, Xinyu Chan, and Gaurav Deshpande from TigerGraph 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

  1. 1. Graph-Powered Machine Learning Methods
    1. Unsupervised Learning with Graph Algorithms
    2. Finding Communities
    3. Finding Similar Things
    4. Finding Frequent Patterns
    5. Summary
    6. Extracting Graph Features
    7. Domain-Independent Features
    8. Domain-Dependent Features
    9. Graph Embeddings: A Whole New World
    10. Summary
    11. Graph Neural Networks
    12. Graph Convolutional Networks
    13. GraphSAGE
    14. Summary
    15. Comparing Graph Machine Learning Approaches
    16. Use Cases for Machine Learning Tasks
    17. Graph-based Learning Methods for Machine Learning Tasks
    18. Graph Neural Networks: Summary and Uses
    19. Chapter Summary
  2. 2. Entity Resolution Revisited
    1. Goal: Identify Real-World Users and Their Tastes
    2. Solution Design
    3. Implementation
    4. Starter Kit
    5. Graph Model
    6. Data Loading
    7. Queries and Analytics
    8. Method 1: Jaccard Similarity
    9. Method 2: Scoring Exact and Approximate Matches
    10. Chapter Summary