book

Graph-Powered Analytics and Machine Learning with TigerGraph

by Victor Lee, Phuc Kien Nguyen, Alexander Thomas

July 2023

Intermediate to advanced

314 pages

8h 28m

English

O'Reilly Media, Inc.

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ObjectivesAudience and PrerequisitesApproach and RoadmapConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
Connections Change EverythingWhat Is a Graph?Why Graphs MatterEdges Outperform Table JoinsGraph Analytics and Machine LearningGraph-Enhanced Machine LearningChapter Summary
Graph StructureGraph TerminologyGraph SchemasTraversing a GraphHops and DistanceBreadth and DepthGraph ModelingSchema Options and Trade-OffsTransforming Tables in a GraphModel EvolutionGraph PowerConnecting the DotsThe 360 ViewLooking Deep for More InsightSeeing and Finding PatternsMatching and MergingWeighing and PredictingChapter Summary
Case 1: Tracing and Analyzing Customer JourneysSolution: Customer 360 + Journey GraphImplementing the C360 + Journey Graph: A GraphStudio TutorialCreate a TigerGraph Cloud AccountGet and Install the Customer 360 Starter KitAn Overview of GraphStudioDesign a Graph SchemaData LoadingQueries and AnalyticsCase 2: Analyzing Drug Adverse ReactionsSolution: Drug Interaction 360 GraphImplementationGraph SchemaQueries and AnalyticsChapter Summary
Goal: Find Promising StartupsSolution: A Startup Investment GraphImplementing a Startup Investment Graph and QueriesThe Crunchbase Starter KitGraph SchemaQueries and AnalyticsChapter Summary
Goal: Detect Financial CrimesSolution: Modeling Financial Crimes as Network PatternsImplementing Financial Crime Pattern SearchesThe Fraud and Money Laundering Detection Starter KitGraph SchemaQueries and AnalyticsChapter Summary
Understanding Graph AnalyticsRequirements for AnalyticsGraph Traversal MethodsParallel ProcessingAggregationUsing Graph Algorithms for AnalyticsGraph Algorithms as ToolsGraph Algorithm CategoriesChapter Summary
Case 1: Improving Healthcare ReferralsSolution: Form and Analyze a Referral GraphImplementing a Referral Network of Healthcare SpecialistsThe Healthcare Referral Network Starter KitGraph SchemaQueries and AnalyticsCase 2: Personalized RecommendationsSolution: Use Graph for Multirelationship-Based RecommendationsImplementing a Multirelationship Recommendation EngineThe Recommendation Engine 2.0 Starter KitGraph SchemaQueries and AnalyticsChapter Summary

The Cost of CyberattacksProblemSolutionImplementing a Cybersecurity GraphThe Cybersecurity Threat Detection Starter KitGraph SchemaQueries and AnalyticsChapter Summary
Goal: Analyzing Airline Flight RoutesSolution: Graph Algorithms on a Flight Route NetworkImplementing an Airport and Flight Route AnalyzerThe Graph Algorithms Starter KitGraph Schema and DatasetInstalling Algorithms from the GDS LibraryQueries and AnalyticsChapter Summary
Unsupervised Learning with Graph AlgorithmsLearning Through Similarity and Community StructureFinding Frequent PatternsExtracting Graph FeaturesDomain-Independent FeaturesDomain-Dependent FeaturesGraph Embeddings: A Whole New WorldGraph Neural NetworksGraph Convolutional NetworksGraphSAGEComparing Graph Machine Learning ApproachesUse Cases for Machine Learning TasksPattern Discovery and Feature Extraction MethodsGraph Neural Networks: Summary and UsesChapter Summary
Problem: Identify Real-World Users and Their TastesSolution: Graph-Based Entity ResolutionLearning Which Entities Are the SameResolving EntitiesImplementing Graph-Based Entity ResolutionThe In-Database Entity Resolution Starter KitGraph SchemaQueries and AnalyticsMethod 1: Jaccard SimilarityMergingMethod 2: Scoring Exact and Approximate MatchesChapter Summary
Goal: Improve Fraud DetectionSolution: Use Relationships to Make a Smarter ModelUsing the TigerGraph Machine Learning WorkbenchSetting Up the ML WorkbenchWorking with ML Workbench and Jupyter NotesGraph Schema and DatasetGraph Feature EngineeringTraining Traditional Models with Graph FeaturesUsing a Graph Neural NetworkChapter SummaryConnecting with You

Overview

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