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https://issues.apache.org/jira/browse/MAHOUT-742?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sebastian Schelter updated MAHOUT-742:
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Resolution: Fixed
Assignee: Sebastian Schelter
Status: Resolved (was: Patch Available)
Patch refactored and committed. Thank you very much for your great work,
Christoph!
> 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
> Assignee: Sebastian Schelter
> Fix For: 0.6
>
> Attachments: MAHOUT-742.patch
>
>
> 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.
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