org.apache.spark.graphx.util

Class GraphGenerators

Object

org.apache.spark.graphx.util.GraphGenerators

public class GraphGenerators
extends Object

A collection of graph generating functions.

Constructor Summary

Constructors

Constructor and Description
GraphGenerators()

Method Summary

Modifier and Type	Method and Description
static Edge<Object>[]	generateRandomEdges(int src, int numEdges, int maxVertexId, long seed)
static Graph<scala.Tuple2<Object,Object>,Object>	gridGraph(SparkContext sc, int rows, int cols) Create rows by cols grid graph with each vertex connected to its row+1 and col+1 neighbors.
static void	initializeForcefully(boolean isInterpreter, boolean silent)
static Graph<Object,Object>	logNormalGraph(SparkContext sc, int numVertices, int numEParts, double mu, double sigma, long seed) Generate a graph whose vertex out degree distribution is log normal.
static void	org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)
static org.slf4j.Logger	org$apache$spark$internal$Logging$$log_()
static double	RMATa()
static double	RMATb()
static double	RMATc()
static double	RMATd()
static Graph<Object,Object>	rmatGraph(SparkContext sc, int requestedNumVertices, int numEdges) A random graph generator using the R-MAT model, proposed in "R-MAT: A Recursive Model for Graph Mining" by Chakrabarti et al.
static Graph<Object,Object>	starGraph(SparkContext sc, int nverts) Create a star graph with vertex 0 being the center.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GraphGenerators

public GraphGenerators()

Method Detail

RMATa

public static double RMATa()

RMATb

public static double RMATb()

RMATd

public static double RMATd()

logNormalGraph

public static Graph<Object,Object> logNormalGraph(SparkContext sc,
                                                  int numVertices,
                                                  int numEParts,
                                                  double mu,
                                                  double sigma,
                                                  long seed)

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.

Parameters:

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

RMATc

public static double RMATc()

generateRandomEdges

public static Edge<Object>[] generateRandomEdges(int src,
                                                 int numEdges,
                                                 int maxVertexId,
                                                 long seed)

rmatGraph

public static Graph<Object,Object> rmatGraph(SparkContext sc,
                                             int requestedNumVertices,
                                             int numEdges)

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.

Parameters:

sc - (undocumented)

requestedNumVertices - (undocumented)

numEdges - (undocumented)

Returns:

(undocumented)

gridGraph

public static Graph<scala.Tuple2<Object,Object>,Object> gridGraph(SparkContext sc,
                                                                  int rows,
                                                                  int cols)

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.

Parameters:

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.

starGraph

public static Graph<Object,Object> starGraph(SparkContext sc,
                                             int nverts)

Create a star graph with vertex 0 being the center.

Parameters:

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.

org$apache$spark$internal$Logging$$log_

public static org.slf4j.Logger org$apache$spark$internal$Logging$$log_()

org$apache$spark$internal$Logging$$log__$eq

public static void org$apache$spark$internal$Logging$$log__$eq(org.slf4j.Logger x$1)

initializeForcefully

public static void initializeForcefully(boolean isInterpreter,
                                        boolean silent)