class MGraphGen[TLabel] extends Error
The MGraphGen object is used to build random graph with various characteristics.
Needs to generate vertex labels of various types including Int, Double,
String, VectorD based on the TLabel type.
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new
MGraphGen(typeSelector: TLabel, stream: Int = 0)(implicit arg0: ClassTag[TLabel])
- typeSelector
the variable for type matches (work around for generic erasure)
- stream
the random number stream to use (0 to 999)
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def
extractSubgraph(size: Int, g: MGraph[TLabel], inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Extracts a subgraph of 'size' vertices from graph 'g' by performing a breadth-first search from a random vertex.
Extracts a subgraph of 'size' vertices from graph 'g' by performing a breadth-first search from a random vertex. FIX - needs testing
- 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
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finalize(): Unit
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def
genBFSQuery(size: Int, avDegree: Int, g: MGraph[TLabel], inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Given a graph 'g', performs a breadth first search starting at a random vertex until the breadth first tree contains 'size' vertices.
Given a graph 'g', performs 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
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def
genPowerLawGraph(size: Int, nLabels: Int, eLabels: Int, maxDegree: Int, distPow: Double = 2.1, inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Generates a graph with power law degree distribution with exponent 'distPow' and uniformly distributed labels.
Generates 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)
- eLabels
the number of edge labels (distributed uniformly)
- maxDegree
the maximum allowed degree for any vertex
- distPow
the power/exponent (2.1 is used in WWW graph pg 72 of m&m graph data)
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
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def
genPowerLawGraph_PowLabels(size: Int, nLabels: Int, eLabels: Int, maxDegree: Int, distPow: Double = 2.1, inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Generates a graph with power law degree distribution with exponent 'distPow' and power law distributed labels.
Generates 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)
- eLabels
the number of edge labels (distributed uniformly)
- maxDegree
the maximum allowed degree for any vertex
- distPow
the power/exponent (2.1 is used in WWW graph pg 72 of m&m graph data)
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
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def
genRandomConnectedGraph(size: Int, nLabels: Int, eLabels: Int, avDegree: Int, inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Generates a random connected graph by using 'genRandomGraph' and checking whether it is connected.
Generates a random connected graph by using 'genRandomGraph' and checking whether it is connected.
- size
the number of vertices to generate
- nLabels
the number of vertex labels (distributed uniformly)
- eLabels
the number of edge labels (distributed uniformly)
- avDegree
the average degree
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
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def
genRandomGraph(size: Int, nLabels: Int, eLabels: Int, avDegree: Int, inverse: Boolean = false, name: String = "g", locality: Boolean = true): MGraph[TLabel]
Generates a random graph with the specified size (number of vertices), average degree and labels evenly distributed across vertices from 0 to 'nLabels - 1'.
Generates 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 vertex labels (distributed uniformly)
- eLabels
the number of edge labels (distributed uniformly)
- avDegree
the average degree
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
- locality
whether to select children from anywhere in graph or locally
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def
genRandomGraph_PowLabels(size: Int, nLabels: Int, eLabels: Int, avDegree: Int, distPow: Double = 2.1, inverse: Boolean = false, name: String = "g"): MGraph[TLabel]
Generates a random graph with labels distributed based on a power law distribution (currently with the magic number 2.1 for the power law exponent).
Generates 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)
- eLabels
the number of edge labels (distributed uniformly)
- avDegree
the average out-degree
- distPow
the power/exponent (2.1 is used in WWW graph pg 72 of m&m graph data)
- inverse
whether to create inverse adjacency (parents)
- name
the name of the graph
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def
setVariate(nLabs: Int): Unit
Set the Random Variate Generator, using the one based on the
TLabeltype.Set the Random Variate Generator, using the one based on the
TLabeltype.- nLabs
the number of labels to generate
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synchronized[T0](arg0: ⇒ T0): T0
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