Analyze your graphs with NetworkX and Kùzu

Dive into the fascinating world of network analysis with this tutorial. In this video, we explore how to leverage the power of Python's NetworkX library a Kùzu graph to analyze a unique graph dataset of Nobel Prize laureates and their mentorship networks. 🏅 Key Highlights: ✅ Why perform network analysis? Discover how graph algorithms can uncover patterns and insights that go beyond basic querying or visualization. ✅ Learn about centrality algorithms like PageRank and Betweenness Centrality and how they reveal influential nodes and key bridges in networks. ✅ See how to transform a Kùzu subgraph into a NetworkX graph, run graph algorithms, and bring the results back into Kùzu for deeper exploration. ✅ Analyze a real-world dataset of 730 Nobel laureates and over 3,000 mentorship trees, tracing connections back hundreds of years. This video only scratches the surface of what can be learned from this dataset, so feel free to check out the code below and explore the Nobel network further! Code to reproduce the analysis: https://github.com/kuzudb/tutorials/tree/main/src/video_7 Additional tutorial in the docs: https://docs.kuzudb.com/get-started/graph-algorithms/ Find us at: Github: https://github.com/kuzudb/kuzu Discord: https://discord.gg/VtX2gw9Rug Twitter: https://x.com/kuzudb