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https://issues.apache.org/jira/browse/SPARK-9975?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-9975:
-----------------------------------
Assignee: Apache Spark
> 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
> Assignee: Apache Spark
> 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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