# Using Graph Databases

Graph database implementation for relationship-heavy data models.

- Repository: https://github.com/ancoleman/ai-design-components (526 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2025-12-11
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add ancoleman/ai-design-components --skill using-graph-databases -g -a claude-code -y`
Codex: `npx skills add ancoleman/ai-design-components --skill using-graph-databases -g -a codex -y`
Cursor: `npx skills add ancoleman/ai-design-components --skill using-graph-databases -g -a cursor -y`
Gemini CLI: `npx skills add ancoleman/ai-design-components --skill using-graph-databases -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ancoleman/ai-design-components`

## Description

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Graph Databases

## Purpose

This skill guides selection and implementation of graph databases for applications where relationships between entities are first-class citizens. Unlike relational databases that model relationships through foreign keys and joins, graph databases natively represent connections as properties, enabling efficient traversal-heavy queries.

## When to Use This Skill

Use graph databases when:
- **Deep relationship traversals** (4+ hops): "Friends of friends of friends"
- **Variable/evolving relationships**: Schema changes don't break existing queries
- **Path finding**: Shortest route, network analysis, dependency chains
- **Pattern matching**: Fraud detection, recommendation engines, access control

**Do NOT use graph databases when**:
- Fixed schema with shallow joins (2-3 tables) → Use PostgreSQL
- Primarily aggregations/analytics → Use columnar databases
- Key-value lookups only → Use Redis/DynamoDB

## Quick Decision Framework

```
DATA CHARACTERISTICS?
├── Fixed schema, shallow joins (≤3 hops)
│   └─ PostgreSQL (relational)
│
├── Already on PostgreSQL + simple graphs
│   └─ Apache AGE (PostgreSQL extension)
│
├── Deep traversals (4+ hops) + general purpose
│   └─ Neo4j (battle-tested, largest ecosystem)
│
├── Multi-model (documents + graph)
│   └─ ArangoDB
│
├── AWS-native, serverless
│   └─ Amazon Neptune
│
└── Real-time streaming, in-memory
    └─ Memgraph
```

## Core Concepts

### Property Graph Model

Graph databases store data as:
- **Nodes** (vertices): Entities with labels and properties
- **Relationships** (edges): Typed connections with properties
- **Properties**: Key-value pairs on nodes and relationships

```
(Person {name: "Alice", age: 28})-[:FRIEND {since: "2020-01-15"}]->(Person {name: "Bob"})
```

### Query Languages

The rest: https://github.com/ancoleman/ai-design-components/blob/HEAD/skills/using-graph-databases/SKILL.md