dynamic.DCSBM: dynamic.DCSBM

In jdwilson4/NetSurv: Network Surveillance via the Degree Corrected Stochastic Block Model

Description

Simulate an ordered sequence of undirected graphs from the degree corrected stochastic block random graph model. Edge weights are discrete valued and are generated independently where e_ij ~ Poisson(theta_i*theta_j*P_c_i, c_j)

Usage

dynamic.DCSBM(n, T, P.array, community.array, delta.array,
  edge.list = c(FALSE, TRUE))

Arguments

n:	number of nodes in the graph
T:	number of graphs in the temporal sequence
P.array:	an array of length T whose tth entry is the matrix of probabilities for network t
community.array:	an array of length T whose tth entry is a numeric vector of length n specifying community labels at time t
delta.array:	an array of length T whose tth entry is a numeric vector of length k whose values must be between 0 and 1.
edge.list:	a logical that specifies whether or not the adjacency matrix should be returned as an edge list.

Value

a list containing the objects

• Adjacency.list: a list of length T whose tth entry is the adjacency matrix (or edge list if edge.list == TRUE) of the tth generated network

• Theta.list: a list of length T whose tth entry is an n x 1 vector with values of each theta for the tth network

Author(s)

James D. Wilson and Nathaniel T. Stevens

References

• Wilson, James D., Stevens, Nathaniel T., and Woodall, William H. (2016). <e2><80><9c>Modeling and estimating change in temporal networks via a dynamic degree corrected stochastic block model.<e2><80><9d> arXiv Preprint: http://arxiv.org/abs/1605.04049

Examples

#Generate a collection of 50 networks with a change at time 25. The change is a local 
#change in connection propensity in community 1
n <- 100
P.old <- cbind(c(0.10, 0.01), c(0.02, 0.075))
P.new <- cbind(c(0.20, 0.025), c(0.02, 0.075))
P.array <- array(c(replicate(25, P.old), replicate(25, P.new)), dim = c(2, 2, 50))
community.array <- array(rep(c(rep(1, 50), rep(2, 50)), 50), dim = c(1, 100, 50))
delta.array <- array(rep(rep(0.2, 2), 50), dim = c(1, 2, 50))

dynamic.net <- dynamic.DCSBM(n = 100, T = 50, P.array = P.array,
                             community.array = community.array,
                             delta.array = delta.array, edge.list = FALSE)
image(Matrix(dynamic.net$Adjacency.list[[1]]))
image(Matrix(dynamic.net$Adjacency.list[[30]]))

jdwilson4/NetSurv documentation built on May 18, 2019, 11:40 p.m.