object PageRank extends Logging
PageRank algorithm implementation. There are two implementations of PageRank implemented.
The first implementation uses the standalone Graph
interface and runs PageRank
for a fixed number of iterations:
var PR = Array.fill(n)( 1.0 ) val oldPR = Array.fill(n)( 1.0 ) for( iter < 0 until numIter ) { swap(oldPR, PR) for( i < 0 until n ) { PR[i] = alpha + (1  alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum } }
The second implementation uses the Pregel
interface and runs PageRank until
convergence:
var PR = Array.fill(n)( 1.0 ) val oldPR = Array.fill(n)( 0.0 ) while( max(abs(PR  oldPr)) > tol ) { swap(oldPR, PR) for( i < 0 until n if abs(PR[i]  oldPR[i]) > tol ) { PR[i] = alpha + (1  \alpha) * inNbrs[i].map(j => oldPR[j] / outDeg[j]).sum } }
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
.
 Source
 PageRank.scala
 Note
This is not the "normalized" PageRank and as a consequence pages that have no inlinks will have a PageRank of alpha.
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def
run[VD, ED](graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Double, Double]
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
 VD
the original vertex attribute (not used)
 ED
the original edge attribute (not used)
 graph
the graph on which to compute PageRank
 numIter
the number of iterations of PageRank to run
 resetProb
the random reset probability (alpha)
 returns
the graph containing with each vertex containing the PageRank and each edge containing the normalized weight.

def
runParallelPersonalizedPageRank[VD, ED](graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15, sources: Array[VertexId])(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Vector, Double]
Run Personalized PageRank for a fixed number of iterations, for a set of starting nodes in parallel.
Run Personalized PageRank for a fixed number of iterations, for a set of starting nodes in parallel. Returns a graph with vertex attributes containing the pagerank relative to all starting nodes (as a sparse vector) and edge attributes the normalized edge weight
 VD
The original vertex attribute (not used)
 ED
The original edge attribute (not used)
 graph
The graph on which to compute personalized pagerank
 numIter
The number of iterations to run
 resetProb
The random reset probability
 sources
The list of sources to compute personalized pagerank from
 returns
the graph with vertex attributes containing the pagerank relative to all starting nodes (as a sparse vector indexed by the position of nodes in the sources list) and edge attributes the normalized edge weight

def
runUntilConvergence[VD, ED](graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Double, Double]
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
 VD
the original vertex attribute (not used)
 ED
the original edge attribute (not used)
 graph
the graph on which to compute PageRank
 tol
the tolerance allowed at convergence (smaller => more accurate).
 resetProb
the random reset probability (alpha)
 returns
the graph containing with each vertex containing the PageRank and each edge containing the normalized weight.

def
runUntilConvergenceWithOptions[VD, ED](graph: Graph[VD, ED], tol: Double, resetProb: Double = 0.15, srcId: Option[VertexId] = None)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Double, Double]
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
Run a dynamic version of PageRank returning a graph with vertex attributes containing the PageRank and edge attributes containing the normalized edge weight.
 VD
the original vertex attribute (not used)
 ED
the original edge attribute (not used)
 graph
the graph on which to compute PageRank
 tol
the tolerance allowed at convergence (smaller => more accurate).
 resetProb
the random reset probability (alpha)
 srcId
the source vertex for a Personalized Page Rank (optional)
 returns
the graph containing with each vertex containing the PageRank and each edge containing the normalized weight.

def
runWithOptions[VD, ED](graph: Graph[VD, ED], numIter: Int, resetProb: Double = 0.15, srcId: Option[VertexId] = None)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Double, Double]
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
 VD
the original vertex attribute (not used)
 ED
the original edge attribute (not used)
 graph
the graph on which to compute PageRank
 numIter
the number of iterations of PageRank to run
 resetProb
the random reset probability (alpha)
 srcId
the source vertex for a Personalized Page Rank (optional)
 returns
the graph containing with each vertex containing the PageRank and each edge containing the normalized weight.

def
runWithOptionsWithPreviousPageRank[VD, ED](graph: Graph[VD, ED], numIter: Int, resetProb: Double, srcId: Option[VertexId], preRankGraph: Graph[Double, Double])(implicit arg0: ClassTag[VD], arg1: ClassTag[ED]): Graph[Double, Double]
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
Run PageRank for a fixed number of iterations returning a graph with vertex attributes containing the PageRank and edge attributes the normalized edge weight.
 VD
the original vertex attribute (not used)
 ED
the original edge attribute (not used)
 graph
the graph on which to compute PageRank
 numIter
the number of iterations of PageRank to run
 resetProb
the random reset probability (alpha)
 srcId
the source vertex for a Personalized Page Rank (optional)
 preRankGraph
PageRank graph from which to keep iterating
 returns
the graph containing with each vertex containing the PageRank and each edge containing the normalized weight.

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