Andra Lungu created FLINK-2310:
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Summary: 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
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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