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Posted to issues@spark.apache.org by "Lee Dongjin (JIRA)" <ji...@apache.org> on 2017/01/06 06:31:01 UTC
[jira] [Commented] (SPARK-15880) PREGEL Based Semi-Clustering
Algorithm Implementation using Spark GraphX API
[ https://issues.apache.org/jira/browse/SPARK-15880?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15803730#comment-15803730 ]
Lee Dongjin commented on SPARK-15880:
-------------------------------------
Hello. It seems like this issue has been abandoned. May I take this? I have an implementation of a Semi-clustering algorithm written in Spark, so I can improve it for SparkML.
> PREGEL Based Semi-Clustering Algorithm Implementation using Spark GraphX API
> ----------------------------------------------------------------------------
>
> Key: SPARK-15880
> URL: https://issues.apache.org/jira/browse/SPARK-15880
> Project: Spark
> Issue Type: New Feature
> Components: GraphX
> Reporter: R J
> Priority: Minor
> Attachments: pregel_paper.pdf
>
> Original Estimate: 672h
> Remaining Estimate: 672h
>
> The main concept of Semi-Clustering algorithm on top of social graphs are:
> - Vertices in a social graph typically represent people, and edges represent connections between them.
> - Edges may be based on explicit actions (e.g., adding a friend in a social networking site), or may be inferred from people’s behaviour (e.g., email conversations or co-publication).
> - Edges may have weights, to represent the interactions frequency or strength.
> - A semi-cluster in a social graph is a group of people who interact frequently with each other and less frequently with others.
> - What distinguishes it from ordinary clustering is that, a vertex may belong to more than one semi-cluster.
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