soal.ppr

BidirectionalPPREstimator

Related Doc: package ppr

class BidirectionalPPREstimator extends AnyRef

Contains methods related to personalized PageRank estimation. All methods operate in the context of the given graph, teleportProbability, and random. See the paper "Personalized PageRank Estimation and Search: A Bidirectional Approach" by Lofgren, Banerjee, and Goel for more information on the algorithm implemented.

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Instance Constructors

  1. new BidirectionalPPREstimator(graph: DirectedGraph, teleportProbability: Float, random: Random = new Random)

Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  8. def estimateMsPerWalk(sourceDistribution: DiscreteDistribution, walkCount: Int = 1000): Float

    Does the given number of walks on the given graph and returns the mean time per walk, in milliseconds.

    Does the given number of walks on the given graph and returns the mean time per walk, in milliseconds. Used to autmatically balance forward and reverse work.

  9. def estimatePPR(sourceDistribution: DiscreteDistribution, targetId: Int, minimumPPR: Float = ..., relativeError: Float = 0.1f, guaranteeRelativeError: Boolean = false): Float

    Estimates the personalized PageRank score from the given source to the given target.

    Estimates the personalized PageRank score from the given source to the given target. The the true score is greater than the given minimumPPR, then the result will have mean relative error less than the given bound. If guaranteeRlativeError is set, then the relative error bound will be guaranteed (at the cost of slower execution); otherwise the relative error is only smaller than the given relativeError bound on average (based on experiments in the above publication).

  10. def estimatePPRSingleSource(sourceId: Int, targetId: Int, minimumPPR: Float = ..., relativeError: Float = 0.1f, guaranteeRelativeError: Boolean = false): Float

    Computes personalized PageRank from a source node to a target node.

    Computes personalized PageRank from a source node to a target node. (Convenience method that calls the above method)

  11. def estimatePPRToTarget(targetId: Int, pprErrorTolerance: Float): Map[Int, Float]

    Given a target nodeId, returns a map estimates such that estimates(v) approximates ppr(v, target) with additive error pprErrorTolerance.

  12. def finalize(): Unit

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  13. final def getClass(): Class[_]

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  14. val graph: DirectedGraph

  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. val random: Random

  21. def samplePPR(sourceDistribution: DiscreteDistribution): Int

    Samples from the PageRank distribution personalized to the given source distribution by doing a single random walk.

  22. def samplePPR(sourceId: Int): Int

    Samples from the PageRank distribution personalized to the given source by doing a single random walk.

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

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  24. val teleportProbability: Float

  25. def toString(): String

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  26. final def wait(): Unit

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