Understanding Complexly Connected Data using Graph Databases
summary
Defines graph vs relational models and explains Neo4j as a native ACID property graph database where relationships are first‑class and can carry properties, spatial and temporal data. Shows real Cypher pattern queries and performance note: traversals follow memory pointers so one hop or twelve hops stay fast. Demos use the Neo4j sandbox criminal dataset (anonymized Greater Manchester 2007), spatial queries (within 500 meters), shortest paths, and community detection, plus a recommendations example (games like Fallout 3 and Borderlands 2). Mentions a 50 graph algorithms library (triangles, betweenness, dangerous‑friends).
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chapters (13)
- 0:00Start
- 2:33History and Background
- 2:49Graph Theory
- 4:38Why Graphs
- 5:52Neo4j What Is a Graph Database
- 7:00The Storage of the Data
- 8:44Graph Model Components
- 10:36Query Language
- 15:58Graph Databases Are Built for Highly Connected Data Problems
- 17:35Criminal Investigation Use Case
- 23:36Community Detection
- 26:14Graph Algorithms
- 29:18Betweenness Centrality
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