Graph generators¶

Using library you can easily generate networks using commonly used models.

Ring¶

Generator creates simple ring network with given number of node.

import ml.sparkling.graph.generators.ring.{RingGenerator, RingGeneratorConfiguration}
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph

implicit ctx:SparkContext=???
// initialize your SparkContext as implicit value

val graph =RingGenerator.generate(RingGeneratorConfiguration(numberOfNodes=5))

// do operations on graph

Network can be also created in undirected version:

import ml.sparkling.graph.generators.ring.{RingGenerator, RingGeneratorConfiguration}
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph

implicit ctx:SparkContext=???
// initialize your SparkContext as implicit value

val graph =RingGenerator.generate(RingGeneratorConfiguration(numberOfNodes=5,undirected = true))

// do operations on graph

Watts And Strogatz¶

Model accepts three parameters:

n - number of nodes

k - mean degree

\(\beta\) - probability of rewiring

Generation is done in two steps:

Ring network with \(n\) nodes is created, each of nodes is connected to \(\frac{k}{2}\) nodes on left and right

Each edge is rewired with probability \(\beta\), where new destination node is seleted randomly from all possible not exsisting connections

For further informations please refer to [Watts]

import ml.sparkling.graph.generators.wattsandstrogatz.{WattsAndStrogatzGenerator, WattsAndStrogatzGeneratorConfiguration}
import org.apache.spark.SparkContext
import org.apache.spark.graphx.Graph

implicit ctx:SparkContext=???
// initialize your SparkContext as implicit value

val graph =WattsAndStrogatzGenerator.generate(WattsAndStrogatzGeneratorConfiguration(numberOfNodes = 10,meanDegree = 2,rewiringProbability = 0.5))

// do operations on graph

References:

[Watts]

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. nature, 393(6684), 440-442. Nature