MCLUtils

Value Members

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

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

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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8. def displayBlockMatrix(mat: BlockMatrix): Unit

9. def displayMatrix(mat: IndexedRowMatrix): Unit

   Print an adjacency matrix in nice format.

   Print an adjacency matrix in nice format.

   mat

   an adjacency matrix

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

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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

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14. def graphOrientationManager(graph: Graph[Int, Double], graphOrientationStrategy: String): RDD[(Int, (Int, Double))]

    Deal with multiple adjacency matrix filling strategy depending on graph orientation

    Deal with multiple adjacency matrix filling strategy depending on graph orientation

    graph

    original graph

    graphOrientationStrategy

    chose a graph strategy completion depending on its nature. 3 choices: undirected, directed, birected.

    returns

    an RDD of new edges weights and associated coordinates.

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. def preprocessGraph[VD](graph: Graph[VD, Double], lookupTable: DataFrame): Graph[Int, Double]

    Get a suitable graph for MCL model algorithm.

    Get a suitable graph for MCL model algorithm.

    Each vertex id in the graph corresponds to a row id in the adjacency matrix.

    graph

    original graph

    lookupTable

    a matching table with nodes ids and new ordered ids

    returns

    prepared graph for MCL algorithm

21. def selfLoopManager(graph: Graph[Int, Double], selfLoopWeight: Double): RDD[(Int, (Int, Double))]

    Deal with self loop

    Deal with self loop

    Add one when weight is nil and remain as it is otherwise

    graph

    original graph

    selfLoopWeight

    a coefficient between 0 and 1 to influence clustering granularity and objective

    returns

    an RDD of self loops weights and associated coordinates.

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

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23. def toGraph(mat: IndexedRowMatrix, vertices: RDD[(VertexId, String)]): Graph[String, Double]

    Transform an IndexedRowMatrix into a Graph

    Transform an IndexedRowMatrix into a Graph

    mat

    an adjacency matrix

    vertices

    vertices of original graph

    returns

    associated graph

24. def toIndexedRowMatrix(graph: Graph[Int, Double], selfLoopWeight: Double, graphOrientationStrategy: String): IndexedRowMatrix

    Transform a Graph into an IndexedRowMatrix

    Transform a Graph into an IndexedRowMatrix

    graph

    original graph

    selfLoopWeight

    a coefficient between 0 and 1 to influence clustering granularity and objective

    graphOrientationStrategy

    chose a graph strategy completion depending on its nature. 3 choices: undirected, directed, birected.

    returns

    a ready adjacency matrix for MCL process.

    To do

    Check graphOrientationStrategy choice for current graph

25. def toString(): String

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26. final def wait(): Unit

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

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

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