Difference between revisions of "Class meeting for 10-605 Subsampling a Graph"
From Cohen Courses
Jump to navigationJump to search (→Slides) |
(→Quiz) |
||
| Line 7: | Line 7: | ||
=== Quiz === | === Quiz === | ||
| − | * [https://qna.cs.cmu.edu/#/pages/view/69 | + | * [https://qna.cs.cmu.edu/#/pages/view/69 Today's quiz] |
=== Readings === | === Readings === | ||
Revision as of 20:23, 19 October 2016
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.
