Advanced Graph Visualizations in Jupyter Notebooks
summary
A Jupyter pipeline uses Neo4j, Graph Data Science and yWorks’ notebook widget to fetch and render live graph snapshots (everything runs locally). Example works on a Discogs subset — 60 artists, 150 groups — and computes PageRank to drive PageRank-driven node sizing and edge thickness scaling. Circular, organic, tree and map heatmap of releases layouts are produced; a path-based timeline encodes artist histories. Most visualizations compute in about 1–2 seconds on the demo machine.
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chapters (8)
- 0:00Introduction to the topic
- 2:39Data pipeline setup
- 6:18Basic graph visualization
- 11:47Question-driven visualization
- 14:02Analyzing graph data science
- 18:55Spatial and heatmap analysis
- 23:22Temporal sequence visualization
- 27:12Conclusion and resources
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