Object

ml.sparkling.graph.operators.partitioning

PSCANBasedPartitioning

Related Doc: package partitioning

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object PSCANBasedPartitioning

Created by Roman Bartusiak (roman.bartusiak@pwr.edu.pl http://riomus.github.io). First approach to community based graph partitioning. It is not efficient due to need of gathering vertex to component id on driver node.

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

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  2. final def ##(): Int

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  4. final def asInstanceOf[T0]: T0

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  5. def buildPartitioningStrategy[ED, VD](graph: Graph[VD, ED], numberOfPartitions: Int, maxIterations: Int = Int.MaxValue)(implicit arg0: ClassTag[ED], arg1: ClassTag[VD], sc: SparkContext): (VertexId, Map[VertexId, Int], Int, ByComponentIdPartitionStrategy)

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  6. def clone(): AnyRef

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  13. val logger: Logger

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. def partitionGraphBy[VD, ED](graph: Graph[VD, ED], numberOfPartitions: Int, maxIterations: Int = Int.MaxValue)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED], sc: SparkContext): Graph[VD, ED]

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  18. def precomputePartitions[ED, VD](graph: Graph[VD, ED], numberOfPartitions: Int, maxIterations: Int = Int.MaxValue)(implicit arg0: ClassTag[ED], arg1: ClassTag[VD], sc: SparkContext): (VertexId, Map[VertexId, Int], Int)

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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  23. final def wait(arg0: Long): Unit

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