class
ParallelPersonalizedPageRank extends Arguments
Value Members

final
def
!=(arg0: Any): Boolean

final
def
##(): Int

def
+(other: String): String


final
def
==(arg0: Any): Boolean

final
def
asInstanceOf[T0]: T0

def
clone(): AnyRef







def
finalize(): Unit

def
formatted(fmtstr: String): String

final
def
getClass(): Class[_]

def
hashCode(): Int

final
def
isInstanceOf[T0]: Boolean



final
def
notify(): Unit

final
def
notifyAll(): Unit




final
def
synchronized[T0](arg0: ⇒ T0): T0

def
toString(): String

final
def
wait(): Unit

final
def
wait(arg0: Long, arg1: Int): Unit

final
def
wait(arg0: Long): Unit

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 insourceIds
. A simple local implementation of this algorithm is as follows.alpha
is the random reset probability (typically 0.15),inNbrs[i]
is the set of neighbors which link toi
andoutDeg[j]
is the out degree of vertexj
.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:
VectorType
): the pageranks of this vertex from all input source verticesThe resulting edges DataFrame contains one additional column:
DoubleType
): the normalized weight of this edge after running PageRank