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https://issues.apache.org/jira/browse/FLINK-2375?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14634119#comment-14634119
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ASF GitHub Bot commented on FLINK-2375:
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Github user shghatge commented on the pull request:
https://github.com/apache/flink/pull/923#issuecomment-123051521
Updated PR
@vasia I removed the Vertex Centric Iteration :) Hope this method is okay.
> Add Approximate Adamic Adar Similarity method using BloomFilters
> ----------------------------------------------------------------
>
> Key: FLINK-2375
> URL: https://issues.apache.org/jira/browse/FLINK-2375
> Project: Flink
> Issue Type: Task
> Components: Gelly
> Reporter: Shivani Ghatge
> 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]
> Using BloomFilters we increase the scalability of the algorithm. The values
> calculated for the edges will be approximate.
> 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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