Graphs: Datastructures to Query
summary
Survey of concrete recipes and results: uses igraph Python/R/C/C++ and an iPython notebook on GitHub, shows a graph-based recommendation built from bipartite person–item joins and a Star Wars projection that cleans a hairball by dropping edges with weight < 4, runs text-rank keyphrases on the PyData page to extract phrases like “analysis tools,” and demonstrates contagion models where a simple SI graph infects in SI vs SIR: 4 steps vs 9 steps after adding node immunity; plotting requires cairo.
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chapters (11)
- 0:00Introduction
- 1:07Graph structure and attributes
- 3:14Graphs and social networks
- 4:18Recommendation systems
- 6:23Node importance in graphs
- 10:37Key phrases via graph metrics
- 13:28Epidemiology with graph agents
- 17:40Python iGraph coding
- 26:35Querying graph data
- 31:03Co-occurrence networks
- 37:07Conference resources
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