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https://issues.apache.org/jira/browse/FLINK-2310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14618466#comment-14618466
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ASF GitHub Bot commented on FLINK-2310:
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GitHub user shghatge opened a pull request:
https://github.com/apache/flink/pull/892
[FLINK-2310] Add an Adamic Adar Similarity example
Added an Adamic Adar Similarity example to Gelly.
Added one test accordingly for the same.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/shghatge/flink adamic
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/892.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #892
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commit a9b83967deb9d8a000286d921a4c04f838a35c4f
Author: Shivani <[email protected]>
Date: 2015-07-08T11:42:51Z
[FLINK-2310] Add an Adamic Adar Similarity example
----
> Add an Adamic-Adar Similarity example
> -------------------------------------
>
> Key: FLINK-2310
> URL: https://issues.apache.org/jira/browse/FLINK-2310
> Project: Flink
> Issue Type: Task
> Components: Gelly
> Reporter: Andra Lungu
> Assignee: Shivani Ghatge
> Priority: Minor
>
> Just as Jaccard, the Adamic-Adar algorithm measures the similarity between a
> set of nodes. However, instead of counting the common neighbors and dividing
> them by the total number of neighbors, the similarity is weighted according
> to the vertex degrees. In particular, it's equal to log(1/numberOfEdges).
> The Adamic-Adar algorithm can be broken into three steps:
> 1). For each vertex, compute the log of its inverse degrees (with the formula
> above) and set it as the vertex value.
> 2). Each vertex will then send this new computed value along with a list of
> neighbors to the targets of its out-edges
> 3). Weigh the edges with the Adamic-Adar index: Sum over n from CN of
> log(1/k_n)(CN is the set of all common neighbors of two vertices x, y. k_n is
> the degree of node n). See [2]
> Prerequisites:
> - Full understanding of the Jaccard Similarity Measure algorithm
> - Reading the associated literature:
> [1] http://social.cs.uiuc.edu/class/cs591kgk/friendsadamic.pdf
> [2]
> http://stackoverflow.com/questions/22565620/fast-algorithm-to-compute-adamic-adar
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