Difference between revisions of "Class meeting for 10-605 Scalable PageRank"
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=== Key things to remember === | === Key things to remember === | ||
| − | * How to implement graph algorithms like PageRank by streaming through a graph, | + | * How to implement graph algorithms like PageRank by streaming through a graph, under various conditions: |
| − | under various conditions: | ||
** Vertex weights fit in memory | ** Vertex weights fit in memory | ||
** Vertex weights do not fit in memory | ** Vertex weights do not fit in memory | ||
| + | * The meaning of various graph statistics: degree distribution, clustering coefficient, ... | ||
| + | * Why sampling from a graph is non-trivial | ||
Revision as of 17:48, 4 December 2015
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2015.
Contents
Slides
- Randomized Algorithms wrap-up, Randomized Algorithms PDF version.
- Tips on Debugging for the SGD Assignment,PDF version
- PageRank and Scalability, PDF version
- Sampling from a Graph, PDF version
Quiz
Readings
- Samping from Large Graphs, Jure Leskovec and Christos Faloutsos, KDD 2006.
- Local Graph Partitioning using PageRank Vectors, Andersen, Chung, Lang, FOCS 2006
- Andersen, Reid, Fan Chung, and Kevin Lang. "Local partitioning for directed graphs using PageRank." Algorithms and Models for the Web-Graph. Springer Berlin Heidelberg, 2007. 166-178.
Key things to remember
- How to implement graph algorithms like PageRank by streaming through a graph, under various conditions:
- Vertex weights fit in memory
- Vertex weights do not fit in memory
- The meaning of various graph statistics: degree distribution, clustering coefficient, ...
- Why sampling from a graph is non-trivial
