com.raphtory.algorithms.generic.dynamic.WattsCascade
WattsCascade
WattsCascade(infectedSeed:Iterable[String], threshold: T = Threshold.UNIFORM_RANDOM, seed:Long = 1, maxGenerations: Int = 100)
run the Watts Cascade dynamic on the network
This algorithm, presented by Duncan Watts in 2002, presents a method for the spread of “infectious ideas.” In the model, people are represented as nodes and relationships are edges. Each node is given a random or deterministic threshold that states it will accept an idea if the fraction its inneighbours accepting the idea has exceeded the threshold.
In the first step the state of all nodes are set. This includes whether they are initially infected and their threshold.
Each noninfected vertex checks whether the number of infected messages it has received outweighs its threshold, if so then it sets its state to be infected and then announces this to all of its outneighbours.
Parameters
infectedSeed: Seq[String]
The list of node names that begin infection.
threshold: Double  Threshold.UNIFORM_RANDOM  Threshold.RANDOM_SAME_VAL = Threshold.UNIFORM_RANDOM
fraction of infected neighbours necessary to trigger change of state. Set to
UNIFORM_RANDOM
to choose thresholds independently at random for each vertex andRANDOM_SAME_VAL
to choose a single random threshold to apply to all vertices..seed: Long
Value used for the random selection, can be set to ensure same result is returned per run. If not specified, it will generate a random seed.
maxGenerations: Int = 100
Maximum number of spreading generations, where seeds are at generation 0.
States
numInfectedNeighbours: Int
Number of infected neighbours
threshold: Double
infection threshold for vertex
infected: Boolean
true if vertex is infected, else false
Returns
vertex name 
infection status 


