Network Analysis Made Simple

on YouTube ↗02h 47m

" summary

Hands-on tutorial that actually shows code and results: builds reproducible notebooks with networkx and nxvis, loads real datasets (a 29‑node seventh‑grade survey with 12 boys and 17 girls, Divvy bike stations), implements BFS exercises, and visualizes graphs with matrix, arc, circle, and hive layouts. Key claims are concrete: adjacency matrix powers count k‑length paths, dot products simulate message passing, and linear‑algebra implementations give ~100× speedup over pure Python for some routines. Includes setup notes (binder, VS Code, conda) and working demos.

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" chapters (39)

1. 0:00Start
2. 10:54Other Examples of Networks
3. 11:07Protein Interaction Networks
4. 19:19Graphs Directionality
5. 22:21Introduction to the Network X Api
6. 25:28Network X Intro
7. 26:00Underlying Data Model
8. 27:00Graph Data Structure
9. 32:00Check How Many Nodes Are Present inside the Graph
10. 33:23Do Nodes Have To Be Integers
11. 39:27Query Edge Information
12. 47:48Add Node
13. 48:18Add Nodes into a Graph
14. 1:05:08Visualizing a Graph
15. 1:06:50Visualizations
16. 1:09:38Matrix Plot
17. 1:15:04Alternative Visualization
18. 1:15:53Circles Plot
19. 1:17:04Hive Plot
20. 1:17:43Clone the Axes
21. 1:22:15Prioritize the Placement of Your Nodes
22. 1:29:48The Original Solution
23. 1:31:16Self-Loops
24. 1:32:49The Degree Centrality Metric
25. 1:33:38Degree Centrality
26. 1:34:20Degree Centrality Function
27. 1:35:12A Plot of the Degree Centrality Metrics
28. 1:36:01The Empirical Cumulative Distribution Function
29. 1:41:31Outlier
30. 1:42:12Visualizing Distributions
31. 1:43:10Preferential Attachment
32. 1:46:53Pattern of Connectivity
33. 1:50:51Paths Notebook
34. 1:53:09The Breadth First Search Algorithm
35. 2:06:40Creating Subgraphs from a Graph
36. 2:12:18Bottleneck Nodes
37. 2:13:38Betweenness Centrality Metric
38. 2:15:58Barbell Graph
39. 2:23:23Questions

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