What Business Can Learn from Dating
summary
Shows how graph databases change recommendations by modeling actual connections and running pattern queries with Neo4j and Cypher; combines social, interest, location and safety graphs (friends‑of‑friends, declared passions, geoproximity, inferred risky behavior) to surface candidates like “Jennifer” from a tiny test set (~100 nodes) and to detect fraud rings across many entities. Claims influence persists to three hops and emphasizes local traversals, real‑time transactional queries and much faster offline bulk loading than online updates for large imports.
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chapters (23)
- 0:00Intro
- 0:36Graph Databases
- 5:59Business Loan from Dating
- 10:30Querying Graph Data
- 15:50Types of Relationships
- 16:53Long Notation
- 17:19Additional Perspectives
- 20:41Safety Graph
- 22:17New Patterns
- 23:41Verification
- 25:35Complex Patterns
- 26:19The Perfect Date
- 27:25Fraud
- 30:07Discrete Data Analysis
- 32:48Network Analysis
- 34:11Fraud Prevention
- 34:39Resources
- 35:39Finding Patterns
- 36:41Graph Visualization
- 37:24Link Curious
- 38:10Machine Learning
- 39:00Domain Expertise
- 42:03Transactional Database
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