See What the Graph Sees: Demystifying PageRank Through Visual Storytelling
summary
Interactive demo shows PageRank implemented step by step on a bike trip graph using NeoDash, Neo4j Bloom, and graph state machines. The graph initializes every station to 1/N (~0.00129), uses damping 0.85, runs up to 20 iterations and converges around 0.0025 for central station 402 while the overall max recorded is 1.83. The build captures dangling mass (8 stations), filters edges with >100 trips, exposes per-iteration ranks, and publishes replayable Cypher and dashboard code in the repo.
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chapters (9)
- 0:00Introduction and setup
- 1:33What is PageRank?
- 3:23Graph model and dataset
- 5:37Classical PageRank approach
- 7:53Step-by-step PageRank visualization
- 10:02Exponential PageRank and iterations
- 12:16Visualizing results in Bloom
- 13:40Reinitializing and experimenting
- 14:28Q&A and resources
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