Big Graph: Big Data applied to giant, dynamic graphs
summary
Covers theory and tools for handling very large, dynamic graphs when full random-access in memory is infeasible. Discusses graph sparsifiers and recent techniques like ℓ0-samplers to approximate k-connectivity and minimum spanning tree weight, enabling incremental and distributed graph-processing without storing the entire graph.
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chapters (8)
- 0:00Introduction
- 2:15DNA sequencing and graphs
- 8:46Graph theory and applications
- 17:52Graphs in commercial databases
- 23:03Challenges of giant dynamic graphs
- 33:05Efficient graph representations
- 37:44Algorithms for dynamic graphs
- 43:51Summary
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