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Chapters

This textbook is organized into 12 chapters covering 200 concepts in graph databases.

Chapter Overview

  1. Introduction to Graph Thinking and Data Modeling - Establishes foundational concepts including data modeling principles, world models, knowledge representation, and core data structures.

  2. Database Systems and NoSQL - Compares RDBMS, OLAP, OLTP, and NoSQL databases to establish why graph databases excel at connected data.

  3. Labeled Property Graph Information Model - Introduces the core LPG model covering nodes, edges, properties, labels, and fundamental graph operations.

  4. Query Languages for Graph Databases - Covers OpenCypher, GSQL, and GQL query languages with comprehensive syntax and optimization techniques.

  5. Performance, Metrics, and Benchmarking - Explores performance fundamentals, indexing strategies, and benchmarking methodologies for graph databases.

  6. Graph Algorithms - Covers essential graph algorithms including search, pathfinding, centrality measures, and graph neural networks.

  7. Social Network Modeling - Applies graph databases to social networks, organizational structures, and human resources applications.

  8. Knowledge Representation and Management - Explores knowledge graphs, ontologies, taxonomies, and enterprise knowledge management systems.

  9. Graph Modeling Patterns and Data Loading - Covers design patterns, anti-patterns, and data loading strategies for graph databases.

  10. Commerce, Supply Chain, and IT Infrastructure - Demonstrates graph applications in e-commerce, supply chain optimization, and IT asset management.

  11. Financial, Healthcare, and Regulatory Applications - Explores domain-specific applications in finance, healthcare, and regulatory compliance.

  12. Advanced Topics and Distributed Systems - Covers distributed graph databases, real-time analytics, and capstone project design.

How to Use This Textbook

Progress through the chapters sequentially, as each chapter builds upon concepts introduced in previous chapters. The textbook follows a carefully designed learning path that respects concept dependencies, ensuring you have the necessary foundation before tackling advanced topics. Early chapters establish core principles, middle chapters explore algorithms and applications, and later chapters cover industry-specific use cases and distributed systems.

Note: Each chapter includes a list of concepts covered. Make sure to complete prerequisites before moving to advanced chapters.