Difference between revisions of "Class meeting for 10-605 Subsampling a Graph"
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=== Slides === | === Slides === | ||
− | * [http://www.cs.cmu.edu/~wcohen/10-605/sampling-a-graph.pptx Powerpoint] | + | * [http://www.cs.cmu.edu/~wcohen/10-605/2016/sampling-a-graph.pptx Powerpoint], [http://www.cs.cmu.edu/~wcohen/10-605/2016/sampling-a-graph.pdf PDF] |
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+ | === Quiz === | ||
+ | |||
+ | * [https://qna.cs.cmu.edu/#/pages/view/69 Today's quiz] | ||
=== Readings === | === Readings === | ||
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* [http://dl.acm.org/citation.cfm?id=1150479 Samping from Large Graphs], Jure Leskovec and Christos Faloutsos, KDD 2006. | * [http://dl.acm.org/citation.cfm?id=1150479 Samping from Large Graphs], Jure Leskovec and Christos Faloutsos, KDD 2006. | ||
* [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 | ||
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+ | Optional: | ||
* [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.] | ||
Latest revision as of 16:44, 1 August 2017
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Contents
Slides
Quiz
Readings
- Samping from Large Graphs, Jure Leskovec and Christos Faloutsos, KDD 2006.
- Local Graph Partitioning using PageRank Vectors, Andersen, Chung, Lang, FOCS 2006
Optional:
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.