GRAPH-POWERED ANALYTICS AND MACHINE LEARNING WITH TIGERGRAPH : driving business outcomes with... connected data

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 Cover Copyright Table of Contents Preface Objectives Audience and Prerequisites Approach and Roadmap Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments Chapter 1. Connections Are Everything Connections Change Everything What Is a Graph? Why Graphs Matter Edges Outperform Table Joins Graph Analytics and Machine Learning Graph-Enhanced Machine Learning Chapter Summary Part I. Connect Chapter 2. Connect and Explore Data Graph Structure Graph Terminology Graph Schemas Traversing a Graph Hops and Distance Breadth and Depth Graph Modeling Schema Options and Trade-Offs Transforming Tables in a Graph Model Evolution Graph Power Connecting the Dots The 360 View Looking Deep for More Insight Seeing and Finding Patterns Matching and Merging Weighing and Predicting Chapter Summary Chapter 3. See Your Customers and Business Better: 360 Graphs Case 1: Tracing and Analyzing Customer Journeys Solution: Customer 360 + Journey Graph Implementing the C360 + Journey Graph: A GraphStudio Tutorial Create a TigerGraph Cloud Account Get and Install the Customer 360 Starter Kit An Overview of GraphStudio Design a Graph Schema Data Loading Queries and Analytics Case 2: Analyzing Drug Adverse Reactions Solution: Drug Interaction 360 Graph Implementation Graph Schema Queries and Analytics Chapter Summary Chapter 4. Studying Startup Investments Goal: Find Promising Startups Solution: A Startup Investment Graph Implementing a Startup Investment Graph and Queries The Crunchbase Starter Kit Graph Schema Queries and Analytics Chapter Summary Chapter 5. Detecting Fraud and Money Laundering Patterns Goal: Detect Financial Crimes Solution: Modeling Financial Crimes as Network Patterns Implementing Financial Crime Pattern Searches The Fraud and Money Laundering Detection Starter Kit Graph Schema Queries and Analytics Chapter Summary Part II. Analyze Chapter 6. Analyzing Connections for Deeper Insight Understanding Graph Analytics Requirements for Analytics Graph Traversal Methods Parallel Processing Aggregation Using Graph Algorithms for Analytics Graph Algorithms as Tools Graph Algorithm Categories Chapter Summary Chapter 7. Better Referrals and Recommendations Case 1: Improving Healthcare Referrals Solution: Form and Analyze a Referral Graph Implementing a Referral Network of Healthcare Specialists The Healthcare Referral Network Starter Kit Graph Schema Queries and Analytics Case 2: Personalized Recommendations Solution: Use Graph for Multirelationship-Based Recommendations Implementing a Multirelationship Recommendation Engine The Recommendation Engine 2.0 Starter Kit Graph Schema Queries and Analytics Chapter Summary Chapter 8. Strengthening Cybersecurity The Cost of Cyberattacks Problem Solution Implementing a Cybersecurity Graph The Cybersecurity Threat Detection Starter Kit Graph Schema Queries and Analytics Chapter Summary Chapter 9. Analyzing Airline Flight Routes Goal: Analyzing Airline Flight Routes Solution: Graph Algorithms on a Flight Route Network Implementing an Airport and Flight Route Analyzer The Graph Algorithms Starter Kit Graph Schema and Dataset Installing Algorithms from the GDS Library Queries and Analytics Chapter Summary Part III. Learn Chapter 10. Graph-Powered Machine Learning Methods Unsupervised Learning with Graph Algorithms Learning Through Similarity and Community Structure Finding Frequent Patterns Extracting Graph Features Domain-Independent Features Domain-Dependent Features Graph Embeddings: A Whole New World Graph Neural Networks Graph Convolutional Networks GraphSAGE Comparing Graph Machine Learning Approaches Use Cases for Machine Learning Tasks Pattern Discovery and Feature Extraction Methods Graph Neural Networks: Summary and Uses Chapter Summary Chapter 11. Entity Resolution Revisited Problem: Identify Real-World Users and Their Tastes Solution: Graph-Based Entity Resolution Learning Which Entities Are the Same Resolving Entities Implementing Graph-Based Entity Resolution The In-Database Entity Resolution Starter Kit Graph Schema Queries and Analytics Method 1: Jaccard Similarity Merging Method 2: Scoring Exact and Approximate Matches Chapter Summary Chapter 12. Improving Fraud Detection Goal: Improve Fraud Detection Solution: Use Relationships to Make a Smarter Model Using the TigerGraph Machine Learning Workbench Setting Up the ML Workbench Working with ML Workbench and Jupyter Notes Graph Schema and Dataset Graph Feature Engineering Training Traditional Models with Graph Features Using a Graph Neural Network Chapter Summary Connecting with You Index About the Authors Colophon 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 learningLearn how graph analytics and machine learning can deliver key business insights and outcomesUse five core categories of graph algorithms to drive advanced analytics and machine learningDeliver a real-time 360-degree view of core business entities, including customer, product, service, supplier, and citizenDiscover insights from connected data through machine learning and advanced analytics