Difference between revisions of "Class meeting for 10-605 Scalable PageRank"
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* [http://www.math.ucsd.edu/~fan/wp/localpartition.pdf Local Graph Partitioning using PageRank Vectors], Andersen, Chung, Lang, FOCS 2006 | * [http://www.math.ucsd.edu/~fan/wp/localpartition.pdf Local Graph Partitioning using PageRank Vectors], Andersen, Chung, Lang, FOCS 2006 | ||
* [http://link.springer.com/chapter/10.1007/978-3-540-77004-6_13#page-1 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.] | * [http://link.springer.com/chapter/10.1007/978-3-540-77004-6_13#page-1 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 | ||
Revision as of 17:47, 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
