com.twitter.cassovary.algorithms.linkanalysis

LinkAnalysis

Related Doc: package linkanalysis

abstract class LinkAnalysis[T <: IterationState] extends AnyRef

All link analysis algorithms should inherit from the LinkAnalysis base class.

T

LinkAnalysis must be generically typed by IterationState or one of its subclasses. An IterationState holds all of the pertinent information for a given algorithm.

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Instance Constructors

  1. new LinkAnalysis(graph: DirectedGraph[Node], params: Params, modelName: String)

    params

    The set of all parameters passed into our algorithm

    modelName

    This must be provided by all subclasses for logging purposes. This should not be left up to the user to define. This must be provided by the implementer.

Abstract Value Members

  1. abstract def defaultInitialState: T

    Provides default initial start values for our algorithms.

    Provides default initial start values for our algorithms.

    returns

    An default starting iteration.

    Attributes
    protected
  2. abstract def iterate(currState: T): T

    Run a single iteration through our algorithm.

    Run a single iteration through our algorithm.

    currState

    The starting iteration that our algorithm will be applied to.

    returns

    A new iteration.

Concrete Value Members

  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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    @throws( ... )
  6. def deltaOfArrays(a: Array[Double], b: Array[Double], t1: Boolean = true): Double

    Calculate the error between two arrays using either the T1 error or the T2 error.

    Calculate the error between two arrays using either the T1 error or the T2 error. This is a convenience method.

    t1

    Flag true to calculate the T1 error (the sum of absolute differences between two arrays). Flag false to calculate the T2 error (the sum of the squared differences between two arrays).

    Attributes
    protected
  7. def efficientNeighborCount(node: Node): Int

    Attributes
    protected
  8. def efficientNeighbors(node: Node): Seq[Int]

    Attributes
    protected
  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. val isInStored: Boolean

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  15. final def isInstanceOf[T0]: Boolean

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  16. val log: Logger

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  17. val maxIterations: Option[Int]

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

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  19. final def notify(): Unit

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  20. final def notifyAll(): Unit

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  21. def postRun(finalState: T): T

    Run final processing of the state.

    Run final processing of the state. Unless the method is overridden, it will just return the final state. If normalization needs to happen upon convergence, this method is the ideal location for such

    finalState
    returns

  22. def run(init: T = defaultInitialState): T

    Run the algorithm to completion according to the parameters passed on instantiation.

    Run the algorithm to completion according to the parameters passed on instantiation.

    init

    The starting point of the iteration. If no iteration is given, the default start is assumed

    returns

    The final iteration.

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

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  24. def toString(): String

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  25. val tolerance: Double

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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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