object GraphGen
The GraphGen object is used to build random graph with various
characteristics.
- Alphabetic
- By Inheritance
- GraphGen
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
extractSubgraph(size: Int, g: Graph, inverse: Boolean = false, name: String = "g"): Graph
Extract a subgraph of 'size' vertices from graph 'g' by performing a breadth-first search from a random vertex.
Extract a subgraph of 'size' vertices from graph 'g' by performing a breadth-first search from a random vertex.
- size
the number of vertices to extract
- g
the data graph to extract from
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
def
genBFSQuery(size: Int, avDegree: Int, g: Graph, inverse: Boolean = false, name: String = "g"): Graph
Given a graph 'g', perform a breadth first search starting at a random vertex until the breadth first tree contains 'size' vertices.
Given a graph 'g', perform a breadth first search starting at a random vertex until the breadth first tree contains 'size' vertices. At each junction, it chooses a random number of children to traverse, with that random number averaging to 'avDegree'.
- size
the number of vertices to extract
- avDegree
the average out degree
- g
the data graph to extract from
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
genPowerLawGraph(size: Int, nLabels: Int, maxDegree: Int, distPow: Double, inverse: Boolean = false, name: String = "g"): Graph
Generate a graph with power law degree distribution with exponent 'distPow' and uniformly distributed labels.
Generate a graph with power law degree distribution with exponent 'distPow' and uniformly distributed labels.
- size
the number of vertices
- nLabels
the number of labels (distributed uniformly)
- maxDegree
the maximum allowed degree for any vertex
- distPow
the power/exponent
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
genPowerLawGraph_PowLabels(size: Int, nLabels: Int, maxDegree: Int, distPow: Double, inverse: Boolean = false, name: String = "g"): Graph
Generate a graph with power law degree distribution with exponent 'distPow' and power law distributed labels.
Generate a graph with power law degree distribution with exponent 'distPow' and power law distributed labels.
- size
the number of vertices
- nLabels
the number of labels (distributed according to power law)
- maxDegree
the maximum allowed degree for any vertex
- distPow
the power/exponent
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
genRandomConnectedGraph(size: Int, nLabels: Int, avDegree: Int, inverse: Boolean = false, name: String = "g"): Graph
Generate a random connected graph by using 'genRandomGraph' and checking whether it is connected.
Generate a random connected graph by using 'genRandomGraph' and checking whether it is connected.
- size
the number of vertices to generate
- nLabels
the number of labels (distributed uniformly)
- avDegree
the average degree
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
genRandomGraph(size: Int, nLabels: Int, avDegree: Int, inverse: Boolean = false, name: String = "g"): Graph
Generate a random graph with the specified size (number of vertices), average degree and labels evenly distributed across vertices from 0 to 'nLabels - 1'.
Generate a random graph with the specified size (number of vertices), average degree and labels evenly distributed across vertices from 0 to 'nLabels - 1'. Not necessarily a connected graph.
- size
the number of vertices to generate
- nLabels
the number of labels (distributed uniformly)
- avDegree
the average degree
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
def
genRandomGraph_PowLabels(size: Int, nLabels: Int, avDegree: Int, inverse: Boolean = false, name: String = "g"): Graph
Generate a random graph with labels distributed based on a power law distribution (currently with the magic number 2.1 for the power law exponent).
Generate a random graph with labels distributed based on a power law distribution (currently with the magic number 2.1 for the power law exponent).
- size
the number of vertices to generate
- nLabels
the number of labels (distributed according to power law)
- avDegree
the average degree
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )