Graph-Powered Analytics and Machine Learning with TigerGraph

Book description

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

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Table of contents

  1. 1. Connect and Explore Data
    1. Graph Structure
      1. Graph Terminology
      2. Graph Schemas
    2. Traversing a Graph
      1. Hops and Distance
      2. Breadth and Depth
    3. Graph Modeling
      1. Schema Options and Tradeoffs
      2. Transforming Tables in a Graph
      3. Model Evolution
    4. Graph Power
      1. Connecting the Dots
      2. The 360 View
      3. Looking Deep for More Insight
      4. Seeing and Finding Patterns
      5. Matching and Merging
      6. Weighing and Predicting
    5. Chapter Summary
  2. 2. See Your Customers and Business Better: 360 Graphs
    1. Case 1: Tracing and Analyzing Customer Journeys
    2. Solution: Customer 360 + Journey Graph
    3. Implementing the C360 + Journey Graph: A GraphStudio Tutorial
      1. Create a TigerGraph Cloud Account
      2. Get and Install the Customer 360 Starter Kit
      3. An Overview of GraphStudio
      4. Design a Graph Schema
      5. Data Loading
      6. Queries and Analytics
    4. Case 2: Analyzing Drug Adverse Reactions
    5. Solution: Drug Interaction 360 Graph
    6. Implementation
      1. Graph Schema
      2. Queries and Analytics
    7. Chapter Summary
  3. 3. Studying Startup Investments
    1. Goal: Find promising startups
    2. Solution: A Startup Investment Graph
    3. Implementing A Startup Investment Graph and Queries
      1. The Crunchbase Starter Kit
      2. Graph Schema
      3. Queries and Analytics
    4. Chapter Summary
  4. 4. Detecting Fraud and Money Laundering Patterns
    1. Goal: Detect Money Laundering
    2. Solution: Modeling Financial Crimes as Network Patterns
    3. Implementing Financial Crime Pattern Searches
      1. The Fraud and Money Laundering Detection Starter Kit
      2. Graph Schema
      3. Queries and Analytics
    4. Chapter Summary
  5. 5. Graph-Powered Machine Learning Methods
    1. Unsupervised Learning with Graph Algorithms
      1. Finding Communities
      2. Finding Similar Things
      3. Finding Frequent Patterns
      4. Summary
    2. Extracting Graph Features
      1. Domain-Independent Features
      2. Domain-Dependent Features
      3. Graph Embeddings: A Whole New World
      4. Summary
    3. Graph Neural Networks
      1. Graph Convolutional Networks
      2. GraphSAGE
      3. Summary
    4. Comparing Graph Machine Learning Approaches
      1. Use Cases for Machine Learning Tasks
      2. Graph-based Learning Methods for Machine Learning Tasks
      3. Graph Neural Networks: Summary and Uses
    5. Chapter Summary
  6. 6. Entity Resolution Revisited
    1. Goal: Identify Real-World Users and Their Tastes
    2. Solution Design
    3. Implementation
      1. Starter Kit
      2. Graph Model
      3. Data Loading
      4. Queries and Analytics
      5. Method 1: Jaccard Similarity
      6. Method 2: Scoring Exact and Approximate Matches
    4. Chapter Summary
  7. About the Authors

Product information

  • Title: Graph-Powered Analytics and Machine Learning with TigerGraph
  • Author(s): Victor Lee, Phuc Kien Nguyen, Xinyu Chang
  • Release date: February 2023
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781098106584