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https://issues.apache.org/jira/browse/SPARK-9975?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15136220#comment-15136220
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Stavros Kontopoulos edited comment on SPARK-9975 at 2/7/16 10:56 AM:
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What do you mean no-one is merging? Is this an abandoned library or frozen, let
me know exactly so not to waste any effort... This is a new feature btw here
and i was planning to fix it, its not an improvement.Betweenness could be also
a feature. Can we contribute to this area or not please let me know...
was (Author: skonto):
What do you mean no-one is merging? Is this an abandoned library or frozen, let
me know exactly so not to waste any effort... This is a new feature btw here
and i was planning to fix it, its not an improvement.Betweenness could be also
a feature. Can we contribute in this area or not please let me know?
> Add Normalized Closeness Centrality to Spark GraphX
> ---------------------------------------------------
>
> Key: SPARK-9975
> URL: https://issues.apache.org/jira/browse/SPARK-9975
> Project: Spark
> Issue Type: New Feature
> Components: GraphX
> Reporter: Kenny Bastani
> Priority: Minor
> Labels: features
>
> “Closeness centrality” is also defined as a proportion. First, the distance
> of a vertex from all other vertices in the network is counted. Normalization
> is achieved by defining closeness centrality as the number of other vertices
> divided by this sum (De Nooy et al., 2005, p. 127). Because of this
> normalization, closeness centrality provides a global measure about the
> position of a vertex in the network, while betweenness centrality is defined
> with reference to the local position of a vertex. -- Cited from
> http://arxiv.org/pdf/0911.2719.pdf
> This request is to add normalized closeness centrality as a core graph
> algorithm in the GraphX library. I implemented this algorithm for a graph
> processing extension to Neo4j
> (https://github.com/kbastani/neo4j-mazerunner#supported-algorithms) and I
> would like to put it up for review for inclusion into Spark. This algorithm
> is very straight forward and builds on top of the included ShortestPaths
> (SSSP) algorithm already in the library.
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