Graph Algorithms. Practical Examples in Apache Spark and Neo4j

Spis treści książki

• Preface
  • Whats in This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• Foreword
• 1. Introduction
  • What Are Graphs?
  • What Are Graph Analytics and Algorithms?
  • Graph Processing, Databases, Queries, and Algorithms
    • OLTP and OLAP
  • Why Should We Care About Graph Algorithms?
  • Graph Analytics Use Cases
  • Conclusion
• 2. Graph Theory and Concepts
  • Terminology
  • Graph Types and Structures
    • Random, Small-World, Scale-Free Structures
  • Flavors of Graphs
    • Connected Versus Disconnected Graphs
    • Unweighted Graphs Versus Weighted Graphs
    • Undirected Graphs Versus Directed Graphs
    • Acyclic Graphs Versus Cyclic Graphs
      • Trees
    • Sparse Graphs Versus Dense Graphs
    • Monopartite, Bipartite, and k-Partite Graphs
  • Types of Graph Algorithms
    • Pathfinding
    • Centrality
    • Community Detection
  • Summary
• 3. Graph Platforms and Processing
  • Graph Platform and Processing Considerations
    • Platform Considerations
    • Processing Considerations
  • Representative Platforms
    • Selecting Our Platform
    • Apache Spark
      • Installing Spark
    • Neo4j Graph Platform
      • Installing Neo4j
  • Summary
• 4. Pathfinding and Graph Search Algorithms
  • Example Data: The Transport Graph
    • Importing the Data into Apache Spark
    • Importing the Data into Neo4j
  • Breadth First Search
    • Breadth First Search with Apache Spark
  • Depth First Search
  • Shortest Path
    • When Should I Use Shortest Path?
    • Shortest Path with Neo4j
    • Shortest Path (Weighted) with Neo4j
    • Shortest Path (Weighted) with Apache Spark
    • Shortest Path Variation: A*
      • A* with Neo4j
    • Shortest Path Variation: Yens k-Shortest Paths
      • Yens with Neo4j
  • All Pairs Shortest Path
    • A Closer Look at All Pairs Shortest Path
    • When Should I Use All Pairs Shortest Path?
    • All Pairs Shortest Path with Apache Spark
    • All Pairs Shortest Path with Neo4j
  • Single Source Shortest Path
    • When Should I Use Single Source Shortest Path?
    • Single Source Shortest Path with Apache Spark
    • Single Source Shortest Path with Neo4j
  • Minimum Spanning Tree
    • When Should I Use Minimum Spanning Tree?
    • Minimum Spanning Tree with Neo4j
  • Random Walk
    • When Should I Use Random Walk?
    • Random Walk with Neo4j
  • Summary
• 5. Centrality Algorithms
  • Example Graph Data: The Social Graph
    • Importing the Data into Apache Spark
    • Importing the Data into Neo4j
  • Degree Centrality
    • Reach
    • When Should I Use Degree Centrality?
    • Degree Centrality with Apache Spark
  • Closeness Centrality
    • When Should I Use Closeness Centrality?
    • Closeness Centrality with Apache Spark
    • Closeness Centrality with Neo4j
    • Closeness Centrality Variation: Wasserman and Faust
    • Closeness Centrality Variation: Harmonic Centrality
      • Harmonic Centrality with Neo4j
  • Betweenness Centrality
    • Bridges and control points
    • Calculating betweenness centrality
    • When Should I Use Betweenness Centrality?
    • Betweenness Centrality with Neo4j
    • Betweenness Centrality Variation: Randomized-Approximate Brandes
      • Random
      • Degree
      • Approximation of Betweenness Centrality with Neo4j
  • PageRank
    • Influence
    • The PageRank Formula
    • Iteration, Random Surfers, and Rank Sinks
    • When Should I Use PageRank?
    • PageRank with Apache Spark
      • PageRank with a fixed number of iterations
      • PageRank until convergence
    • PageRank with Neo4j
    • PageRank Variation: Personalized PageRank
      • Personalized PageRank with Apache Spark
  • Summary
• 6. Community Detection Algorithms
  • Example Graph Data: The Software Dependency Graph
    • Importing the Data into Apache Spark
    • Importing the Data into Neo4j
  • Triangle Count and Clustering Coefficient
    • Local Clustering Coefficient
    • Global Clustering Coefficient
    • When Should I Use Triangle Count and Clustering Coefficient?
    • Triangle Count with Apache Spark
    • Triangles with Neo4j
    • Local Clustering Coefficient with Neo4j
  • Strongly Connected Components
    • When Should I Use Strongly Connected Components?
    • Strongly Connected Components with Apache Spark
    • Strongly Connected Components with Neo4j
  • Connected Components
    • When Should I Use Connected Components?
    • Connected Components with Apache Spark
    • Connected Components with Neo4j
  • Label Propagation
    • Semi-Supervised Learning and Seed Labels
    • When Should I Use Label Propagation?
    • Label Propagation with Apache Spark
    • Label Propagation with Neo4j
  • Louvain Modularity
    • Quality-based grouping via modularity
    • When Should I Use Louvain?
    • Louvain with Neo4j
  • Validating Communities
  • Summary
• 7. Graph Algorithms in Practice
  • Analyzing Yelp Data with Neo4j
    • Yelp Social Network
    • Data Import
    • Graph Model
    • A Quick Overview of the Yelp Data
    • Trip Planning App
      • Finding influential hotel reviewers
    • Travel Business Consulting
      • Bellagio cross-promotion
    • Finding Similar Categories
  • Analyzing Airline Flight Data with Apache Spark
    • Exploratory Analysis
    • Popular Airports
    • Delays from ORD
    • Bad Day at SFO
    • Interconnected Airports by Airline
    • Summary
• 8. Using Graph Algorithms to Enhance Machine Learning
  • Machine Learning and the Importance of Context
    • Graphs, Context, and Accuracy
  • Connected Feature Extraction and Selection
    • Graphy Features
    • Graph Algorithm Features
  • Graphs and Machine Learning in Practice: Link Prediction
    • Tools and Data
    • Importing the Data into Neo4j
    • The Coauthorship Graph
    • Creating Balanced Training and Testing Datasets
      • Balancing and splitting data
    • How We Predict Missing Links
    • Creating a Machine Learning Pipeline
    • Predicting Links: Basic Graph Features
    • Predicting Links: Triangles and the Clustering Coefficient
    • Predicting Links: Community Detection
  • Summary
  • Wrapping Things Up
• A. Additional Information and Resources
  • Other Algorithms
  • Neo4j Bulk Data Import and Yelp
  • APOC and Other Neo4j Tools
  • Finding Datasets
  • Assistance with the Apache Spark and Neo4j Platforms
  • Training
• Index

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