org.apache.spark.graphx.util

GraphGenerators

object GraphGenerators extends Logging

A collection of graph generating functions.

Source
GraphGenerators.scala
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  6. val RMATa: Double

  7. val RMATb: Double

  8. val RMATc: Double

  9. val RMATd: Double

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  15. def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int, seed: Long = 1): Array[Edge[Int]]

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

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  17. def gridGraph(sc: SparkContext, rows: Int, cols: Int): Graph[(Int, Int), Double]

    Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors.

    Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors. Vertex ids are assigned in row major order.

    sc

    the spark context in which to construct the graph

    rows

    the number of rows

    cols

    the number of columns

    returns

    A graph containing vertices with the row and column ids as their attributes and edge values as 1.0.

  18. def hashCode(): Int

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  19. final def isInstanceOf[T0]: Boolean

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  20. def isTraceEnabled(): Boolean

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  21. def log: Logger

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  22. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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  23. def logDebug(msg: ⇒ String): Unit

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  24. def logError(msg: ⇒ String, throwable: Throwable): Unit

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  25. def logError(msg: ⇒ String): Unit

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  26. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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  28. def logName: String

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  29. def logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int = 0, mu: Double = 4.0, sigma: Double = 1.3, seed: Long = 1): Graph[Long, Int]

    Generate a graph whose vertex out degree distribution is log normal.

    Generate a graph whose vertex out degree distribution is log normal.

    The default values for mu and sigma are taken from the Pregel paper:

    Grzegorz Malewicz, Matthew H. Austern, Aart J.C Bik, James C. Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing. SIGMOD '10.

    If the seed is -1 (default), a random seed is chosen. Otherwise, use the user-specified seed.

    sc

    Spark Context

    numVertices

    number of vertices in generated graph

    numEParts

    (optional) number of partitions

    mu

    (optional, default: 4.0) mean of out-degree distribution

    sigma

    (optional, default: 1.3) standard deviation of out-degree distribution

    seed

    (optional, default: -1) seed for RNGs, -1 causes a random seed to be chosen

    returns

    Graph object

  30. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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  31. def logTrace(msg: ⇒ String): Unit

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  32. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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  33. def logWarning(msg: ⇒ String): Unit

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  35. final def notify(): Unit

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  36. final def notifyAll(): Unit

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  37. def rmatGraph(sc: SparkContext, requestedNumVertices: Int, numEdges: Int): Graph[Int, Int]

    A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.

    A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.

    See http://www.cs.cmu.edu/~christos/PUBLICATIONS/siam04.pdf.

  38. def starGraph(sc: SparkContext, nverts: Int): Graph[Int, Int]

    Create a star graph with vertex 0 being the center.

    Create a star graph with vertex 0 being the center.

    sc

    the spark context in which to construct the graph

    nverts

    the number of vertices in the star

    returns

    A star graph containing nverts vertices with vertex 0 being the center vertex.

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

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  40. def toString(): String

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