Graph Analytics - From the Whiteboard to Your Toolbox
summary
Claims graph framing gives practical wins and shows exactly how to model problems, what code looks like, and what breaks. Examples convert social, web-navigation, logistics and scheduling problems into graphs, store weights on edges, run Dijkstra shortest paths, compute maximum bipartite matchings, and use a greedy coloring that produces three colors for the schedule. Demonstrations use NetworkX, compare igraph and graph-tool, and mention Neo4j and installation/performance tradeoffs.
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chapters (7)
- 0:00Graph analytics intro
- 7:45Finding graph problems
- 13:58Conference scheduling model
- 16:06Python graph libraries
- 21:22NetworkX graph analysis
- 25:54Graph study resources
- 27:19Q&A on graphs
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