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Posted to issues@spark.apache.org by "Stavros Kontopoulos (JIRA)" <ji...@apache.org> on 2016/02/07 11:56:40 UTC
[jira] [Comment Edited] (SPARK-9975) Add Normalized Closeness
Centrality to Spark GraphX
[ https://issues.apache.org/jira/browse/SPARK-9975?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15136220#comment-15136220 ]
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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