[jira] [Updated] (MAHOUT-742) Pagerank implementation in Map/Reduce

["Christoph Nagel (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-742?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Christoph Nagel updated MAHOUT-742:
-----------------------------------

    Attachment: MAHOUT-742.patch

> 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
>         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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