Kenny Bastani created SPARK-9975:
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Summary: 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
“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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