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Sean Owen commented on SPARK-9975: ---------------------------------- Yeah, that's why I replied. I have seen a number of PRs for GraphX not be reviewed for months, so I'm not sure what's going on there. Unless it's a clear and important fix I don't know that others would review it. I don't know if there are more formal conclusions to draw, but I'm trying to save you time. > 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org