Use Cases

Discover how Geode’s graph database capabilities solve real-world problems across industries. From social networks to fraud detection, knowledge graphs to supply chain optimization, graph databases excel at representing and querying connected data.

Overview

Graph databases are uniquely suited for applications where relationships between entities are as important as the entities themselves. Geode’s property graph model, combined with powerful GQL query capabilities, enables natural and efficient solutions for complex, connected data problems.

This section explores common use cases with practical examples, schema designs, and query patterns. Each use case demonstrates how graph databases provide advantages over traditional relational or document databases.

Why Graph Databases?

Traditional Databases vs. Graph Databases

Relational Databases:

  • Require complex JOINs for relationships
  • Performance degrades with deep traversals
  • Schema changes difficult for evolving relationships
  • NOT optimized for connected data queries

Graph Databases:

  • ✅ Relationships are first-class citizens
  • ✅ Constant-time traversals regardless of depth
  • ✅ Flexible schema for evolving relationships
  • ✅ Optimized for pattern matching queries

When to Use Graph Databases

Choose graph databases when:

  • Relationships Matter: Data has rich, complex relationships
  • Deep Traversals: Queries involve multi-hop navigation
  • Pattern Matching: Need to find specific graph patterns
  • Network Analysis: Analyzing networks, flows, or hierarchies
  • Evolving Schema: Relationships change frequently

Topics in This Section

  • Use Cases Overview - Introduction to graph database use cases with pattern examples
  • Social Networks - Friend networks, activity feeds, community detection, and influence analysis
  • Fraud Detection - Transaction networks, pattern detection, and fraud ring identification
  • Knowledge Graphs - Enterprise knowledge management, semantic search, and relationship discovery
  • Recommendation Engines - Collaborative filtering, content-based recommendations, and hybrid approaches
  • Supply Chain - Supplier networks, logistics optimization, and disruption analysis

Use Case Highlights

Social Networks

Build rich social experiences with graph-native operations.

Key Capabilities:

  • Friend-of-friend recommendations
  • Activity feed generation
  • Community detection
  • Influence analysis
  • Content sharing networks

Example Query:

-- Find mutual friends
MATCH (me:Person {name: "Alice"})-[:KNOWS]->(friend)
      -[:KNOWS]->(mutual)<-[:KNOWS]-(me)
RETURN mutual.name, count(*) AS mutual_friends
GROUP BY mutual.name
ORDER BY mutual_friends DESC;

Why Graphs: Social networks are inherently graphs. Graph databases make queries like “friend of friend” natural and performant, while relational databases require complex self-joins that degrade quickly.

See: Social Networks Use Case

Fraud Detection

Identify suspicious patterns and fraud rings in financial networks.

Key Capabilities:

  • Transaction pattern analysis
  • Fraud ring detection
  • Anomaly detection
  • Risk scoring
  • Real-time alerts

Example Query:

-- Find suspicious transaction chains
MATCH path = (a:Account)-[t:TRANSFER*2..5]->(b:Account)
WHERE all(tx in t WHERE tx.amount > 10000)
  AND duration.between(t[0].timestamp, t[-1].timestamp) < duration({hours: 1})
RETURN path, [tx in t | tx.amount] AS amounts;

Why Graphs: Fraud patterns often involve networks of accounts and transactions. Graph queries naturally express patterns like “circular transfers” or “layered transactions” that are difficult in relational databases.

See: Fraud Detection Use Case

Knowledge Graphs

Connect information across enterprise systems for semantic search and discovery.

Key Capabilities:

  • Entity relationship mapping
  • Semantic search
  • Knowledge discovery
  • Context understanding
  • Question answering

Example Query:

-- Find experts on a topic
MATCH (person:Person)-[:AUTHORED]->(doc:Document)
      -[:ABOUT]->(topic:Topic {name: "Machine Learning"})
MATCH (doc)-[:CITES]->(cited:Document)
      -[:ABOUT]->(topic)
RETURN person.name, count(DISTINCT doc) AS documents,
       count(DISTINCT cited) AS citations
ORDER BY citations DESC, documents DESC
LIMIT 10;

Why Graphs: Knowledge graphs represent how information connects across domains. Graph traversals enable discovery of indirect relationships and context that traditional search misses.

See: Knowledge Graphs Use Case

Recommendation Engines

Generate personalized recommendations based on user behavior and item relationships.

Key Capabilities:

  • Collaborative filtering
  • Content-based recommendations
  • Hybrid approaches
  • Real-time personalization
  • Cold start handling

Example Query:

-- Recommend products based on similar users
MATCH (me:User {id: $user_id})-[:PURCHASED]->(product)
      <-[:PURCHASED]-(similar:User)
MATCH (similar)-[:PURCHASED]->(recommendation)
WHERE NOT (me)-[:PURCHASED]->(recommendation)
RETURN recommendation.name,
       count(DISTINCT similar) AS similarity_score,
       avg(recommendation.rating) AS avg_rating
ORDER BY similarity_score DESC, avg_rating DESC
LIMIT 10;

Why Graphs: Recommendations rely on understanding relationships between users, items, and behaviors. Graph databases make collaborative filtering queries natural and efficient.

See: Recommendation Engines Use Case

Supply Chain Optimization

Model and analyze complex supply networks for optimization and risk management.

Key Capabilities:

  • Supplier network mapping
  • Logistics optimization
  • Disruption impact analysis
  • Route planning
  • Inventory tracking

Example Query:

-- Find alternative suppliers
MATCH path = (supplier:Supplier)-[:SUPPLIES*1..3]->(product:Product {id: $product_id})
WHERE supplier.status = 'active'
  AND all(r in relationships(path) WHERE r.lead_time < 30)
RETURN supplier.name,
       length(path) AS supply_chain_depth,
       reduce(time = 0, r in relationships(path) | time + r.lead_time) AS total_lead_time
ORDER BY total_lead_time, supply_chain_depth;

Why Graphs: Supply chains are networks of suppliers, manufacturers, and distributors. Graph queries efficiently analyze multi-tier dependencies and alternative routes.

See: Supply Chain Use Case

Industry Applications

Financial Services

  • Fraud Detection: Transaction pattern analysis
  • Risk Assessment: Credit risk networks
  • Compliance: AML (Anti-Money Laundering) monitoring
  • Customer 360: Unified customer view

E-Commerce & Retail

  • Recommendations: Product and content recommendations
  • Customer Journey: Path analysis and optimization
  • Inventory: Multi-location inventory tracking
  • Loyalty: Customer relationship networks

Healthcare

  • Patient Networks: Care coordination and referrals
  • Drug Interactions: Medication interaction graphs
  • Epidemiology: Disease spread modeling
  • Research: Clinical trial networks

Technology & IT

  • Network Management: Infrastructure topology
  • Dependency Tracking: Service dependencies
  • Access Control: Permission hierarchies
  • Knowledge Management: Internal wikis and docs

Telecommunications

  • Network Topology: Physical and logical networks
  • Call Detail Records: Call pattern analysis
  • Customer Churn: Social influence on churn
  • Service Provisioning: Resource allocation

Manufacturing

  • Bill of Materials: Product component hierarchies
  • Supply Chain: Multi-tier supplier networks
  • Quality: Defect tracking and root cause
  • Maintenance: Asset relationship tracking

Performance Characteristics

Graph databases excel at different query patterns:

Graph Database Advantages

Fast for:

  • ✅ Multi-hop traversals (friend-of-friend)
  • ✅ Pattern matching (fraud patterns)
  • ✅ Relationship-heavy queries
  • ✅ Recursive queries
  • ✅ Network analysis

Example: Finding friends-of-friends

  • Graph DB: Constant time per hop (milliseconds for 10 hops)
  • Relational DB: O(n^hops) with joins (seconds to minutes)

Relational Database Advantages

Fast for:

  • Tabular aggregations (SUM, AVG across rows)
  • Simple equality lookups with indexes
  • Set-based operations on single table
  • Report generation from denormalized tables

Use the right tool: Some applications benefit from both graph and relational databases working together.

Schema Design Patterns

Star Schema Pattern 

-- Central entity with relationships
(:User)-[:PURCHASED]->(:Product)
(:User)-[:VIEWED]->(:Product)
(:User)-[:RATED]->(:Product)

Network Pattern 

-- Network of interconnected entities
(:Person)-[:KNOWS]->(:Person)
(:Person)-[:WORKS_FOR]->(:Company)
(:Company)-[:PARTNERED_WITH]->(:Company)

Hierarchy Pattern 

-- Tree or DAG structures
(:Category)-[:PARENT]->(:Category)
(:Employee)-[:REPORTS_TO]->(:Manager)
(:Concept)-[:IS_A]->(:Concept)

Temporal Pattern 

-- Time-based relationships
(:User)-[:PURCHASED {timestamp: ...}]->(:Product)
(:Document)-[:VERSION {version: ..., date: ...}]->(:Document)

Getting Started with Your Use Case

1. Identify Graph Patterns

Ask these questions:

  • What are the entities (nodes)?
  • How are they connected (relationships)?
  • What properties describe entities and relationships?
  • What queries will you run most frequently?

2. Design Schema

  • Choose meaningful labels (:Person, :Product, :Account)
  • Model relationships explicitly
  • Add properties for attributes
  • Consider indexes for frequent queries

3. Load Sample Data 

-- Start small with representative data
CREATE (:Entity1 {property: value});
CREATE (:Entity2 {property: value});
MATCH (e1:Entity1), (e2:Entity2)
CREATE (e1)-[:RELATIONSHIP]->(e2);

4. Test Queries 

-- Verify queries work as expected
MATCH pattern
WHERE conditions
RETURN results;

5. Optimize Performance

  • Create indexes for frequent filters
  • Use EXPLAIN to understand query plans
  • Profile queries with PROFILE
  • Adjust schema if needed

6. Scale Up

  • Load full dataset
  • Monitor performance metrics
  • Optimize based on real workload
  • Consider distributed deployment

Best Practices by Use Case

Social Networks

  • Index user IDs and usernames
  • Use materialized views for activity feeds
  • Implement pagination for large result sets
  • Cache popular queries (trending content)

Fraud Detection

  • Real-time query execution
  • Combine graph patterns with ML models
  • Use RLS for data isolation
  • Implement alerting for suspicious patterns

Knowledge Graphs

  • Rich metadata on relationships
  • Full-text indexes for semantic search
  • Version entities for change tracking
  • Link to external data sources

Recommendations

  • Pre-compute similarity scores
  • Use hybrid approach (collaborative + content)
  • Implement A/B testing framework
  • Track recommendation performance

Supply Chain

  • Model temporal aspects (dates, versions)
  • Implement scenario analysis
  • Track confidence levels on data
  • Real-time disruption alerts

Learn More

Example Datasets

Explore use cases with sample data:

  • Social Network: 1,000 users, 5,000 relationships
  • E-Commerce: 10,000 products, 50,000 orders
  • Knowledge Graph: 5,000 documents, 20,000 relationships
  • Supply Chain: 500 suppliers, 1,000 products

Contact for access to sample datasets.

Next Steps

  1. Choose Your Use Case: Select the most relevant use case
  2. Read Detailed Guide: Explore the use case documentation
  3. Try Sample Queries: Run examples with sample data
  4. Design Your Schema: Model your specific domain
  5. Build Your Application: Integrate with client libraries

Contributing

Have a use case to share? Contributions welcome!

See Contributing Guide for how to submit new use cases or improvements.

Pages

Use Case Guides

Practical guides to modeling and querying real-world domains in Geode, mapping features like embeddings, vector search, security, and analytics to business use cases

2 min read

Fraud and Anomaly Detection

Detect fraudulent transactions using graph patterns, ML embeddings, real-time CDC analytics, and Row-Level Security for compliance

6 min read

Recommendation Systems

Build personalized recommendation engines using collaborative filtering, graph embeddings, and vector similarity search with Geode

7 min read

Knowledge Graphs and Semantic Networks

Build knowledge graphs with Geode for semantic search, ontology management, RDF integration, and intelligent information retrieval

10 min read

Social Network Analysis

Build and analyze social networks with Geode: community detection, influence analysis, viral content tracking, and real-time engagement metrics

16 min read

Supply Chain Optimization

Optimize supply chain operations with Geode: route planning, inventory tracking, supplier networks, and real-time logistics analytics

11 min read