Object

ml.sparkling.graph.operators.partitioning

CommunityBasedPartitioning

Related Doc: package partitioning

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object CommunityBasedPartitioning

Created by Roman Bartusiak ([email protected] 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. case class ByComponentIdPartitionStrategy(idMap: Broadcast[Map[VertexId, ComponentID]]) extends PartitionStrategy with Product with Serializable

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

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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  6. final def eq(arg0: AnyRef): Boolean

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  7. def equals(arg0: Any): Boolean

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  8. def finalize(): Unit

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

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  10. def hashCode(): Int

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  11. final def isInstanceOf[T0]: Boolean

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

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  13. final def notify(): Unit

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  14. final def notifyAll(): Unit

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  15. def partitionGraphBy[VD, ED](graph: Graph[VD, ED], communityDetectionMethod: CommunityDetectionAlgorithm)(implicit arg0: ClassTag[VD], arg1: ClassTag[ED], sc: SparkContext): Graph[VD, ED]

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  16. def partitionGraphBy[VD, ED](graph: Graph[VD, ED], communityDetectionMethod: CommunityDetectionMethod[VD, ED])(implicit arg0: ClassTag[VD], arg1: ClassTag[ED], sc: SparkContext): Graph[VD, ED]

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

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  18. def toString(): String

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  19. final def wait(): Unit

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

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

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