GraphGenerators

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. val RMATa: Double

7. val RMATb: Double

8. val RMATc: Double

9. val RMATd: Double

10. final def asInstanceOf[T0]: T0

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11. def clone(): AnyRef

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

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13. def equals(arg0: Any): Boolean

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14. def finalize(): Unit

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15. def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int): 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 logNormalGraph(sc: SparkContext, numVertices: Int, numEParts: Int, mu: Double = 4.0, sigma: Double = 1.3): Graph[Long, Int]

    Generate a graph whose vertex out degree is log normal.

    Generate a graph whose vertex out degree 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.

    sc

    numVertices

    mu

    sigma

    returns

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

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22. final def notify(): Unit

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23. final def notifyAll(): Unit

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24. 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.

25. 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.

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

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27. def toString(): String

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28. final def wait(): Unit

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

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

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