Warming up to Graphs
summary
Quick primer on graphs then hands‑on work showing a Neo4j prototype using Python to persist and query a few thousand generated users and projects with directed relationships, labels, and properties. Demonstrates creating nodes and rels via py2neo, running Cypher queries for recommendations and shortestPath traversals, and notes performance: constant time for immediate neighbors, linear for long traversals. Mentions Neo4j limits (billions of nodes on one machine), community vs commercial HA, and when graphs fit or fail (social, recommendations, fraud vs write‑heavy logs).
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chapters (15)
- 0:00Start
- 0:22What is a Graph?
- 1:30Some Graph Theory
- 5:34Graph Algorithms
- 6:04Dijkstra's Algorithm A search algorithm
- 10:19networkx.github.io
- 36:44Performance.
- 38:50Scenarios
- 39:21Social Graphs
- 39:50Fraud Detection
- 40:10Recommendations
- 40:34Dependencies
- 41:21When is it a Bad Idea?
- 42:10Tabular Data?
- 43:01neo4django - Neo4j Models for Django
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