object GraphGenerators extends Logging
A collection of graph generating functions.
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- val RMATa: Double
- val RMATb: Double
- val RMATc: Double
- val RMATd: Double
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- def generateRandomEdges(src: Int, numEdges: Int, maxVertexId: Int, seed: Long = -1): Array[Edge[Int]]
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def
gridGraph(sc: SparkContext, rows: Int, cols: Int): Graph[(Int, Int), Double]
Create
rows
bycols
grid graph with each vertex connected to its row+1 and col+1 neighbors.Create
rows
bycols
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.
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log: Logger
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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
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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.
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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.
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