Difference between revisions of "Class meeting for 10-605 Scalable PageRank"
From Cohen Courses
Jump to navigationJump to search| Line 24: | Line 24: | ||
** Vertex weights do not fit in memory | ** Vertex weights do not fit in memory | ||
* The meaning of various graph statistics: degree distribution, clustering coefficient, ... | * The meaning of various graph statistics: degree distribution, clustering coefficient, ... | ||
| − | * Why sampling from a graph is non-trivial | + | * Why sampling from a graph is non-trivial if you want to preserve properties of the graph like |
| + | ** Degree distribution | ||
| + | ** Homophily as measured by clustering coefficient, | ||
| + | * What local graph partitioning is and how the PageRank-Nibble algorithm, together with sweeps to optimize conductance, can be used to approximately solve it. | ||
| + | * The implications of the analysis of PageRank-Nibble. | ||
Revision as of 18:25, 6 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 if you want to preserve properties of the graph like
- Degree distribution
- Homophily as measured by clustering coefficient,
- What local graph partitioning is and how the PageRank-Nibble algorithm, together with sweeps to optimize conductance, can be used to approximately solve it.
- The implications of the analysis of PageRank-Nibble.
