Neo4j Basics

Overview

Neo4j is a graph database designed to efficiently store, query, and traverse graph data.
It is based on the property graph model, making it ideal for social networks, recommendation engines, fraud detection, and knowledge graphs.

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1. Property Graph Model

Components

| Component | Description | |———–|————-| | Node | Represents an entity (e.g., Person, Product). | | Relationship | Connects nodes and describes the type of connection (e.g., FRIENDS_WITH). | | Properties | Key-value pairs associated with nodes or relationships (e.g., name: "Alice"). | | Labels | Categorize nodes for easy querying (e.g., :Person, :Movie). |

Example

(:Person {name: "Alice", age: 30})-[:FRIENDS_WITH]->(:Person {name: "Bob", age: 25})

• Node Alice is labeled Person with properties name and age.
• Relationship FRIENDS_WITH connects Alice to Bob.

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2. Neo4j Architecture

• Native Graph Storage: Stores nodes and relationships directly, optimized for graph traversals.
• ACID-compliant: Ensures atomicity, consistency, isolation, and durability.
• Indexing: Supports indexes on node labels and properties for faster queries.
• Traversal Engine: Efficiently follows relationships for queries like shortest path or pattern matching.

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3. Cypher Query Language

Cypher is Neo4j’s declarative query language, optimized for graph patterns.

3.1 Basic Queries

Create Nodes

CREATE (a:Person {name: 'Alice', age: 30})
CREATE (b:Person {name: 'Bob', age: 25})

Create Relationships

MATCH (a:Person {name: 'Alice'}), (b:Person {name: 'Bob'})
CREATE (a)-[:FRIENDS_WITH]->(b)

Read Nodes

MATCH (p:Person)
RETURN p.name, p.age

Read Relationships

MATCH (a:Person)-[r:FRIENDS_WITH]->(b:Person)
RETURN a.name, b.name

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3.2 Filtering and Conditions

MATCH (p:Person)
WHERE p.age > 25
RETURN p.name, p.age

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3.3 Updating Data

MATCH (p:Person {name: 'Alice'})
SET p.age = 31
RETURN p

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3.4 Deleting Nodes and Relationships

MATCH (p:Person {name: 'Bob'})
DETACH DELETE p

DETACH DELETE removes the node and all its relationships.

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4. Common Graph Operations

Operation	Cypher Example
Find friends of a person	MATCH (a:Person {name:'Alice'})-[:FRIENDS_WITH]->(b) RETURN b.name
Shortest path	MATCH p=shortestPath((a:Person {name:'Alice'})-[:FRIENDS_WITH*]-(b:Person {name:'Eve'})) RETURN p
Count nodes	MATCH (p:Person) RETURN count(p)
Aggregation	MATCH (p:Person) RETURN avg(p.age)

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5. Indexes and Constraints

• Create Index

CREATE INDEX FOR (p:Person) ON (p.name)

• Create Unique Constraint

CREATE CONSTRAINT unique_name ON (p:Person) ASSERT p.name IS UNIQUE

• Indexes speed up lookups, and constraints ensure data integrity.

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6. Use Cases

• Social Networks: Friend/follower graphs
• Recommendation Engines: Product or content recommendations based on relationships
• Fraud Detection: Identify suspicious patterns in financial transactions
• Knowledge Graphs: Connect entities and relationships for semantic queries
• Network & IT Ops: Dependency and infrastructure mapping

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7. Best Practices

• Model data as nodes and relationships, avoid flattening graphs into tables.
• Use labels and properties wisely for efficient queries.
• Apply indexes on frequently queried properties.
• Use DETACH DELETE cautiously to avoid accidental data loss.
• Leverage pattern matching in Cypher for complex traversals instead of iterative queries.

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References

• Neo4j Official Documentation
• Cypher Query Language Guide
• Neo4j Property Graph Model
• Graph Data Modeling Best Practices

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