class ParallelPersonalizedPageRank extends Arguments
Parallel Personalized PageRank algorithm implementation.
This implementation uses the standalone GraphFrame interface and runs personalized PageRank
in parallel for a fixed number of iterations. This can be run by setting maxIter
. The source
vertex Ids are set in sourceIds
. A simple local implementation of this algorithm is as
follows.
var oldPR = Array.fill(n)( 1.0 ) val PR = (0 until n).map(i => if sourceIds.contains(i) alpha else 0.0) for( iter <- 0 until maxIter ) { swap(oldPR, PR) for( i <- 0 until n ) { PR[i] = (1 - alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum if (sourceIds.contains(i)) PR[i] += alpha } }
alpha
is the random reset probability (typically 0.15), inNbrs[i]
is the set of neighbors
which link to i
and outDeg[j]
is the out degree of vertex j
.
Note that this is not the "normalized" PageRank and as a consequence pages that have no inlinks will have a PageRank of alpha. In particular, the pageranks may have some values greater than 1.
The resulting vertices DataFrame contains one additional column:
- pageranks (
VectorType
): the pageranks of this vertex from all input source vertices
The resulting edges DataFrame contains one additional column:
- weight (
DoubleType
): the normalized weight of this edge after running PageRank
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ensuring(cond: (ParallelPersonalizedPageRank) ⇒ Boolean, msg: ⇒ Any): ParallelPersonalizedPageRank
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maxIter(value: Int): ParallelPersonalizedPageRank.this.type
Number of iterations to run
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def
resetProbability(value: Double): ParallelPersonalizedPageRank.this.type
Reset probability "alpha"
- def run(): GraphFrame
-
def
sourceIds(values: Array[Any]): ParallelPersonalizedPageRank.this.type
Source vertices for a Personalized Page Rank
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(Since version 2.12.16) Use
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