object Graphs extends Graphs
Example GraphFrames for testing the API
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- def ALSSyntheticData(): GraphFrame
Some synthetic data that sits in Spark.
Some synthetic data that sits in Spark.
No description available.
- Definition Classes
- Graphs
- final def asInstanceOf[T0]: T0
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- def chain(n: Long): GraphFrame
Returns a chain graph of the given size with Long ID type.
Returns a chain graph of the given size with Long ID type. The vertex IDs are 0, 1, ..., n-1, and the edges are (0, 1), (1, 2), ...., (n-2, n-1).
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- Graphs
- def clone(): AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
- def empty[T](implicit arg0: scala.reflect.api.JavaUniverse.TypeTag[T]): GraphFrame
Returns an empty GraphFrame of the given ID type.
Returns an empty GraphFrame of the given ID type.
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- Graphs
- final def eq(arg0: AnyRef): Boolean
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- def friends: GraphFrame
Graph of friends in a social network.
Graph of friends in a social network.
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- Graphs
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- def gridIsingModel(spark: SparkSession, n: Int): GraphFrame
Version of
gridIsingModelwith vStd, eStd set to 1.0.Version of
gridIsingModelwith vStd, eStd set to 1.0.- Definition Classes
- Graphs
- def gridIsingModel(spark: SparkSession, n: Int, vStd: Double, eStd: Double): GraphFrame
This method generates a grid Ising model with random parameters.
This method generates a grid Ising model with random parameters.
Ising models are probabilistic graphical models over binary variables xi. Each binary variable xi corresponds to one vertex, and it may take values -1 or +1. The probability distribution P(X) (over all xi) is parameterized by vertex factors ai and edge factors bij:
P(X) = (1/Z) * exp[ \sum_i a_i x_i + \sum_{ij} b_{ij} x_i x_j ]where Z is the normalization constant (partition function). See Wikipedia for more information on Ising models.
Each vertex is parameterized by a single scalar ai. Each edge is parameterized by a single scalar bij.
- n
Length of one side of the grid. The grid will be of size n x n.
- vStd
Standard deviation of normal distribution used to generate vertex factors "a". Default of 1.0.
- eStd
Standard deviation of normal distribution used to generate edge factors "b". Default of 1.0.
- returns
GraphFrame. Vertices have columns "id" and "a". Edges have columns "src", "dst", and "b". Edges are directed, but they should be treated as undirected in any algorithms run on this model. Vertex IDs are of the form "i,j". E.g., vertex "1,3" is in the second row and fourth column of the grid.
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- Graphs
- def hashCode(): Int
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- final def notifyAll(): Unit
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- def star(n: Long): GraphFrame
Returns a star graph with Long ID type, consisting of a central element indexed 0 (the root) and the n other leaf vertices 1, 2, ..., n.
Returns a star graph with Long ID type, consisting of a central element indexed 0 (the root) and the n other leaf vertices 1, 2, ..., n.
- n
the number of leaves
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- Graphs
- final def synchronized[T0](arg0: => T0): T0
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- def toString(): String
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- def twoBlobs(blobSize: Int): GraphFrame
Two densely connected blobs (vertices 0->n-1 and n->2n-1) connected by a single edge (0->n)
Two densely connected blobs (vertices 0->n-1 and n->2n-1) connected by a single edge (0->n)
- blobSize
the size of each blob.
- Definition Classes
- Graphs
- final def wait(arg0: Long, arg1: Int): Unit
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