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| Foreword | |
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| Preface | |
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| Introduction | |
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| What Is a Graph? | |
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| A High-Level View of the Graph Space | |
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| Graph Databases | |
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| Graph Compute Engines | |
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| The Power of Graph Databases | |
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| Performance | |
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| Flexibility | |
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| Agility | |
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| Summary | |
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| Options for Storing Connected Data | |
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| Relational Databases Lack Relationships | |
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| NOSQL Databases Also Lack Relationships | |
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| Graph Databases Embrace Relationships | |
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| Summary | |
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| Data Modeling with Graphs | |
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| Models and Goals | |
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| The Property Graph Model | |
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| Querying Graphs: An Introduction to Cypher | |
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| Cypher Philosophy | |
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| START | |
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| MATCH | |
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| RETURN | |
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| Other Cypher Clauses | |
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| A Comparison of Relational and Graph Modeling | |
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| Relational Modeling in a Systems Management Domain | |
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| Graph Modeling in a Systems Management Domain | |
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| Testing the Model | |
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| Cross-Domain Models | |
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| Creating the Shakespeare Graph | |
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| Beginning a Query | |
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| Declaring Information Patterns to Find | |
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| Constraining Matches | |
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| Processing Results | |
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| Query Chaining | |
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| Common Modeling Pitfalls | |
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| Email Provenance Problem Domain | |
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| A Sensible First Iteration? | |
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| Second Time's the Charm | |
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| Evolving the Domain | |
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| Avoiding Anti-Patterns | |
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| Summary | |
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| Building a Graph Database Application | |
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| Data Modeling | |
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| Describe the Model in Terms of the Application's Needs | |
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| Nodes for Things, Relationships for Structure | |
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| Fine-Grained versus Generic Relationships | |
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| Model Facts as Nodes | |
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| Represent Complex Value Types as Nodes | |
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| Time | |
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| Iterative and Incremental Development | |
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| Application Architecture | |
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| Embedded Versus Server | |
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| Clustering | |
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| Load Balancing | |
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| Testing | |
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| Test-Driven Data Model Development | |
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| Performance Testing | |
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| Capacity Planning | |
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| Optimization Criteria | |
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| Performance | |
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| Redundancy | |
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| Load | |
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| Summary | |
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| Graphs in the Real World | |
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| Why Organizations Choose Graph Databases | |
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| Common Use Cases | |
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| Social | |
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| Recommendations | |
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| Geo | |
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| Master Data Management | |
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| Network and Data Center Management | |
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| Authorization and Access Control (Communications) | |
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| Real-World Examples | |
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| Social Recommendations (Professional Social Network) | |
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| Authorization and Access Control | |
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| Geo (Logistics) | |
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| Summary | |
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| Graph Database Internals | |
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| Native Graph Processing | |
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| Native Graph Storage | |
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| Programmatic APIs | |
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| Kernel API | |
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| Core (or "Beans") API | |
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| Traversal API | |
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| Nonfunctional Characteristics | |
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| Transactions | |
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| Recoverability | |
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| Availability | |
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| Scale | |
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| Summary | |
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| Predictive Analysis with Graph Theory | |
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| Depth-and Breadth-First Search | |
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| Path-Finding with Dijkstra's Algorithm | |
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| The A* Algorithm | |
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| Graph Theory and Predictive Modeling | |
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| Triadic Closures | |
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| Structural Balance | |
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| Local Bridges | |
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| Summary | |
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| NOSQL Overview | |