Pagerank implementation in Map/Reduce
-------------------------------------
Key: MAHOUT-742
URL: https://issues.apache.org/jira/browse/MAHOUT-742
Project: Mahout
Issue Type: New Feature
Components: Graph
Affects Versions: 0.6
Reporter: Christoph Nagel
Hi,
my name is Christoph Nagel. I'm student on technical university Berlin and
participating on the course of Isabel Drost and Sebastian Schelter.
My work is to implement the pagerank-algorithm, where the pagerank-vector fits
in memory.
For the computation I used the naive algorithm shown in the book 'Mining of
Massive Datasets' from Rajaraman & Ullman
(http://www-scf.usc.edu/~csci572/2012Spring/UllmanMiningMassiveDataSets.pdf).
Matrix- and vector-multiplication are done with mahout methods.
Most work is the transformation the input graph, which has to consists of a
nodes- and edges file.
Format of nodes file: <node>\n
Format of edges file: <startNode>\t<endNode>\n
Therefore I created the following classes:
* LineIndexer: assigns each line an index
* EdgesToIndex: indexes the nodes of the edges
* EdgesIndexToTransitionMatrix: creates the transition matrix
* Pagerank: computes PR from transition matrix
* JoinNodesWithPagerank: creates the joined output
* PagerankExampleJob: does the complete job
Each class has a test (not PagerankExampleJob) and I took the example of the
book for evaluating.
--
This message is automatically generated by JIRA.
For more information on JIRA, see: http://www.atlassian.com/software/jira
