Neo4j Graph Database

Neo4j is a prominent graph database management system designed to store, manage, and query data using a graph structure composed of nodes, relationships, and properties. Unlike traditional relational databases that rely on tables and complex joins, Neo4j is optimized for handling highly connected datasets by treating relationships as first-class citizens, enabling rapid traversal and analysis of complex networks. It utilizes a powerful, declarative query language called Cypher, making it an ideal solution for applications such as social networks, recommendation engines, fraud detection, and knowledge graphs, where understanding the connections between data points is paramount.

1. Introduction to Graph Databases and Neo4j
   1. Limitations of Relational Databases for Connected Data
      1. Data Modeling Challenges
      2. Performance Bottlenecks with Joins
      3. Schema Rigidity
      4. Difficulty in Querying Relationships
   2. What is a Graph Database
      1. Definition and Characteristics
      2. Graph Database vs Relational Database
      3. Graph Database vs Document Database
      4. Graph Database vs Key-Value Store
      5. Benefits of Graph Databases
   3. Core Concepts of Graph Theory
      1. Vertices (Nodes)
         1. Definition and Role
         2. Node Properties
         3. Real-World Examples
      2. Edges (Relationships)
         1. Definition and Role
         2. Directed Relationships
         3. Undirected Relationships
         4. Relationship Properties
      3. Paths
         1. Definition of a Path
         2. Path Traversal Concepts
         3. Path Length
         4. Shortest Paths
      4. Graph Structures
         1. Directed vs Undirected Graphs
         2. Cyclic vs Acyclic Graphs
         3. Connected Components
         4. Subgraphs
   4. Use Cases for Graph Databases
      1. Social Networks
         1. Modeling Friendships and Follows
         2. Community Detection
         3. Influence Analysis
      2. Recommendation Engines
         1. Collaborative Filtering
         2. Content-Based Recommendations
         3. Real-Time Recommendations
      3. Fraud Detection
         1. Pattern Recognition
         2. Anomaly Detection
         3. Risk Assessment
      4. Knowledge Graphs
         1. Semantic Relationships
         2. Data Integration
         3. Entity Resolution
      5. Network and IT Operations
         1. Network Topology Mapping
         2. Impact Analysis
         3. Dependency Management
      6. Supply Chain and Logistics
         1. Route Optimization
         2. Supplier Networks
      7. Master Data Management
         1. Data Lineage
         2. Reference Data Management
   5. Introduction to Neo4j
      1. History and Evolution
         1. Origins of Neo4j
         2. Major Release Milestones
         3. Current Version Features
      2. Neo4j's Place in the DBMS Landscape
         1. Comparison with Other Graph Databases
         2. Market Position
         3. Adoption in Industry
      3. The Labeled Property Graph Model
         1. Structure and Components
         2. Advantages of LPG Model
         3. LPG vs RDF Comparison