Graph-Powered Analytics and Machine Learning with TigerGraph - Helion

ebook

Autor: Victor Lee Ph. D, Phuc Kien Nguyen, Alexander Thomas
ISBN: 9781098106607
stron: 316, Format: ebook
Data wydania: 2023-07-24
Księgarnia: Helion

Cena książki: 203,15 zł (poprzednio: 236,22 zł)
Oszczędzasz: 14% (-33,07 zł)

Osoby, które kupiły tę książkę, wybierały także »

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

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Spis treści

Graph-Powered Analytics and Machine Learning with TigerGraph eBook -- spis treści

Preface
- Objectives
- Audience and Prerequisites
- Approach and Roadmap
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. Connections Are Everything
- Connections Change Everything
  - What Is a Graph?
  - Why Graphs Matter
    - Structure matters
    - Communities matter
    - Patterns of connections matter
  - Edges Outperform Table Joins
- Graph Analytics and Machine Learning
  - Graph-Enhanced Machine Learning
- Chapter Summary
I. Connect
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
    - Vertex, edge, or property?
    - Edge direction
    - Granularity of edge type
    - Modeling interaction events
    - Adjusting your design schema based on use case
  - Transforming Tables in a Graph
    - Optimizing mapping choices
  - 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
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
    - Deploy a cloud instance with a starter kit
    - Alternative: Import a starter kit into your TigerGraph instance
    - Load data and install queries for a starter kit
  - An Overview of GraphStudio
  - Design a Graph Schema
  - Data Loading
  - Queries and Analytics
    - Customer interaction subgraph
    - Customer journey
    - Similar customers
- Case 2: Analyzing Drug Adverse Reactions
- Solution: Drug Interaction 360 Graph
- Implementation
  - Graph Schema
  - Queries and Analytics
    - Find similar reported cases
    - Most reported drug for a company
    - Top side effects for top drugs
- Chapter Summary
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
    - Key role discovery
    - Investor successful exits
    - Top startups based on board
    - Top startups based on leader
- Chapter Summary
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
    - Invited user behavior
    - Multitransaction
    - Circle detection
- Chapter Summary
II. Analyze
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
    - Path and tree algorithms
    - Centrality algorithms
    - Community algorithms
    - Similarity algorithms
    - Neighborhood similarity
    - Jaccard similarity
    - Cosine similarity
    - Role similarity
    - SimRank
    - RoleSim
    - Classification and prediction algorithms
- Chapter Summary
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
    - Get common patients
    - Infer the referral network
    - Find influential doctors
    - Find a referral community
- 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
    - Recommend by features and context
    - Recommend products by customer and context
    - Get top demographics
- Chapter Summary
8. Strengthening Cybersecurity
- The Cost of Cyberattacks
- Problem
- Solution
- Implementing a Cybersecurity Graph
  - The Cybersecurity Threat Detection Starter Kit
  - Graph Schema
  - Queries and Analytics
    - Detect bypassing of a firewall
    - Suspicious IP detection
    - Flooding detection
    - Footprint detection
    - Tracing the source of an alert
- Chapter Summary
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
    - Calculate route length
    - Measure and analyze centrality
    - Find shortest paths
    - Modify a GSQL algorithm to customize the output
    - Find and analyze communities
- Chapter Summary
III. Learn
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
    - Graphlets
    - Graph algorithms
  - Domain-Dependent Features
  - Graph Embeddings: A Whole New World
    - Random walk-based embeddings
    - DeepWalk
    - Node2vec
- 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
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
    - Initialization
    - Similarity detection
  - Merging
    - Reset
  - Method 2: Scoring Exact and Approximate Matches
    - Initialization
    - Scoring weighted exact matches
    - Scoring approximate matches
    - Merging similar entities
- Chapter Summary
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
    - Create a TigerGraph Cloud ML Bundle
    - Create and copy database credentials
    - Connect the ML Workbench to your graph database
  - 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