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From "Apache Spark (JIRA)" <>
Subject [jira] [Assigned] (SPARK-9975) Add Normalized Closeness Centrality to Spark GraphX
Date Fri, 14 Aug 2015 10:09:46 GMT


Apache Spark reassigned SPARK-9975:

    Assignee: Apache Spark

> Add Normalized Closeness Centrality to Spark GraphX
> ---------------------------------------------------
>                 Key: SPARK-9975
>                 URL:
>             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
> 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 (
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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