Neo4j Live: Inside StrangerGraphs – Predicting Season 5 with Graph Intelligence
Join us as we break down StrangerGraphs, a prediction graph built from Reddit fan theories, Neo4j AuraDB, GPT-5 analysis, and GraphRAG-powered agents to explore what the Stranger Things community got right in past seasons - and what they might reveal about Season 5.
Key Highlights:
- Reddit prediction mining + GPT-5 accuracy scoring
- Leiden clustering to find high-signal predictor communities
- Season 5 predictions extracted from accuracy-based hubs
- AuraDB + GraphRAG powering character-aware AI agents
Guest: Henry Collie
StrangerGraphs: https://strangergraphs.com/
Blog: https://neo4j.com/blog/news/hopper-graph/
Chapters
- 00:00Welcome & Intro
- 03:05Graphs as digital twins & learning through play
- 06:25Graph data science as “alchemy” (communities & interpretation)
- 10:06Origin of Stranger Graphs & prediction idea
- 12:20Live demo: character agents & graph-powered predictions
- 17:48Exploring the graph: prediction communities & accuracy
- 21:35Pipeline overview: scraping Reddit & fandom data
- 27:09Chunking, classification & managing LLM costs
- 34:04Similarity graphs, communities & prediction hubs
- 46:09From predictions to outcomes: matching plot points
- 59:36Q&A
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