> For the complete documentation index, see [llms.txt](https://george-jen.gitbook.io/data-science-and-apache-spark/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://george-jen.gitbook.io/data-science-and-apache-spark/class-graphgenerators.md).

# Class GraphGenerators

```
Object
  org.apache.spark.graphx.util.GraphGenerators
```

Constructor:

GraphGenerators()

Methods:

```
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 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 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.
```


---

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