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https://issues.apache.org/jira/browse/FLINK-2310?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14662844#comment-14662844
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ASF GitHub Bot commented on FLINK-2310:
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Github user andralungu commented on the pull request:
https://github.com/apache/flink/pull/892#issuecomment-128923829
Hi @shghatge,
Remember the git rebase command we talked about when you were in Berlin?
You should revert the last commit and rebase instead... right now it looks like
15 other people contributed to your code ;) This is because you merged the
master into your branch.
> 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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