Estimation for multipartite SBM
estimateMultipartiteSBM(listSBM, estimOptions = list())list of networks that were defined by the defineSBM function
options for the inference procedure
a MultipartiteSBM_fit object with the estimated parameters and the blocks in each Functional Group
The list of parameters estimOptions essentially tunes the optimization process and the variational EM algorithm, with the following parameters
"nbCores" integer for number of cores used. Default is 2
"verbosity" integer for verbosity (0, 1). Default is 1
"nbBlocksRange" List of length the number of functional groups, each element supplying the minimal and maximal number of blocks to be explored. The names of the list must be the names of the functional groups. Default value is from 1 to 10)
"initBM" Boolean. True if using simple and bipartite SBM as initialisations. Default value = TRUE
"maxiterVEM" Number of max. number of iterations in the VEM. Default value = 100
"maxiterVE" Number of max. number of iterations in the VE. Default value = 100
if (FALSE) {
## About the Parts/Functional Groups (FG)
blockProp <- list(c(0.16 ,0.40 ,0.44),c(0.3,0.7)) # prop of blocks in each FG
archiMultipartite <- rbind(c(1,2),c(2,2),c(1,1)) # architecture of the multipartite net.
nbNodes <- c(60,50)
## About the connection matrices
directed <- c(NA, TRUE, FALSE) # type of each network
model <- c('gaussian','bernoulli','poisson')
C1 <-
list(mean = matrix(c(6.1, 8.9, 6.6, 9.8, 2.6, 1.0), 3, 2),
var = matrix(c(1.6, 1.6, 1.8, 1.7 ,2.3, 1.5),3, 2))
C2 <- list(mean = matrix(c(0.7,1.0, 0.4, 0.6),2, 2))
m3 <- matrix(c(2.5, 2.6 ,2.2 ,2.2, 2.7 ,3.0 ,3.6, 3.5, 3.3),3,3 )
C3 <- list(mean = .5 * (m3 + t(m3)))
connectParam <- list(C1, C2, C3)
## Graph Sampling
mySampleMSBM <- sampleMultipartiteSBM(nbNodes, blockProp,
archiMultipartite, connectParam, model,
directed, dimLabels = c('A','B'), seed = 2)
listSBM <- mySampleMSBM$listSBM
estimOptions <- list(initBM = FALSE, nbCores = 2)
myMSBM <- estimateMultipartiteSBM(listSBM, estimOptions)
plot(myMSBM, type = "data")
plot(myMSBM, type = "expected")
plot(myMSBM, type = "meso")
}