graph-databases

## What I do - Query graph databases efficiently - Design graph schemas and models - Traverse complex relationships - Optimize graph queries - Build recommendation systems ## When to use me When working with highly connected data, social networks, fraud detection, or recommendation engines. ## Graph Concepts ### Nodes and Relationships - **Nodes**: Entities (people, products, places) - **Relationships**: Connections between nodes - **Properties**: Key-value pairs on both - **Labels**: Node types - **Types**: Relationship types ### Property Graph Model ```cypher CREATE (alice:Person {name: 'Alice'})-[:FRIEND {since: 2020}]->(bob:Person {name: 'Bob'}) ``` ## Cypher Query Language ### Basic Queries ```cypher // Match nodes MATCH (p:Person) WHERE p.name = 'Alice' RETURN p // Relationships MATCH (p1:Person)-[:FRIEND]->(p2:Person) RETURN p1, p2 // Pattern matching MATCH (p:Person)-[:FRIEND]->(friend)-[:FRIEND]->(friendOfFriend) WHERE p.name = 'Alice' RETURN friendOfFriend.name ``` ### Filtering ```cypher MATCH (p:Person) WHERE p.age > 25 AND p.name STARTS WITH 'A' RETURN p ``` ### Aggregation ```cypher MATCH (p:Person)-[:FRIEND]->(friend) WITH p, count(friend) AS friendCount WHERE friendCount > 10 RETURN p ``` ### Path Finding ```cypher // Shortest path MATCH path = shortestPath((a:Person)-[*]-(b:Person)) WHERE a.name = 'Alice' AND b.name = 'Charlie' RETURN path ``` ## Graph Modeling ### Design Principles - Start with questions, not entities - Use meaningful relationship types - Model for queries - Consider traversal depth - Denormalize appropriately ### Common Patterns - **Friend of Friend**: Social connections - **Hierarchies**: Org charts, categories - **Sequences**: User journeys, events - **Multiple hops**: N-degree connections ## Use Cases ### Social Networks - Friend recommendations - Influence analysis - Community detection ### Fraud Detection - Unusual patterns - Ring detection - Connection analysis ### Recommendation Engines - "Users who bought this also bought" - Skill matching - Content recommendations ### Network Analysis - IT infrastructure - Supply chain - Disease spread ### Knowledge Graphs - Semantic search - Entity resolution - Taxonomy ## Database Systems - **Neo4j**: Most popular, Cypher - **Amazon Neptune**: Multi-model (graph + RDF) - **ArangoDB**: Multi-model (graph + document) - **Apache Jena**: RDF triple store - **TigerGraph**: High performance ## Performance Optimization - Indexes on properties - Relationship density consideration - Avoid excessive traversal - Use projections - Partition large graphs