essc: essc

Description Usage Arguments Value Author(s) References Examples

Description

Identify statistically significant communities in undirected networks

Usage

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essc(Adj.Matrix, alpha, Null = c("Binomial", "Poisson"),
  Num.Samples = nrow(Adj.Matrix))

Arguments

Adj.Matrix

Adjacency matrix of the network for which you'd like to find communities

alpha

False discovery rate. Value between 0 and 1 that sets an upper limit on the false discovery rate of the hypothesis testing procedure in ESSC

Null

The null distribution used for comparing the observed number of connections between a single vertex and a community of vertices. Can be set to either "Binomial" or "Poisson." Default is "Binomial"

Num.Samples

The number of randomly selected neighborhoods used to initiate the ESSC algorithm. The maximum number is the number of vertices in Adj.Matrix. Defaults to the number of vertices in Adj.Matrix

Value

a list containing the objects

Author(s)

James D. Wilson

References

Examples

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net <- stochastic.block(n = 1000, k = 3, P = cbind(c(0.1, 0.01, 0.01), c(0.01, 0.1, 0.01), c(0.01, 0.01, 0.1)), sizes = c(300, 300, 400))
results <- essc(net$Adjacency, alpha = 0.10, Null = "Poisson")
print(results$Communities)

rykim2/ESSCDocProj documentation built on May 23, 2019, 3:07 p.m.