Tutorial

library(MLVSBM)

The package deals with multilevel network defined as the junction of two interaction network (adjacency matrices) linked by an affiliation relationship (affiliation matrix).

First, we’re going to simulate a multilevel network with 100 individuals and 3 clusters of individuals for the lower level and 50 organizations and 3 clusters for the upper level. The inter-organizational level will have an assortative structure and will be undirected, the inter-individual’s one a core-periphery structure and will be directed. Affiliation matrix will be generated by a power law and the dependency between the latent blocks of the two levels will be strong.

set.seed(123)
my_mlvsbm <- MLVSBM::mlvsbm_simulate_network(
  n = list(I = 60, O = 40), # Number of nodes for the lower level and the upper level
  Q = list(I = 3, O = 3), # Number of blocks for the lower level and the upper level
  pi = c(.5, .3, .2), # Block proportion for the upper level, must sum to one
  gamma = matrix(c(.8, .1, .1,  # Block proportion for the lower level,
                   .1, .8, .1,
                   .1, .1, .8), # each column must sum to one
                 nrow = 3, ncol = 3, byrow = TRUE),  
  alpha = list(I = matrix(c(.1, .1, .3, 
                            .1, .2, .5,
                            .1, .5, .5), 
                          nrow = 3, ncol = 3, byrow = TRUE), # Connection matrix
               O = matrix(c(.5, .1, .1, 
                            .1, .5, .1,
                            .1, .1, .5), 
                          nrow = 3, ncol = 3, byrow = TRUE)),# between blocks
  directed = list(I = TRUE, O = FALSE), # Are the upper and lower level directed or not ?
  affiliation = "preferential", # How the affiliation matrix is generated
  no_empty_org = FALSE) # May the affiliation matrix have column suming to 0

The network is stocked in an R6 object of type MLVSBM.

Now, we are going to create a multilevel network object from 2 existing adjacency matrix and an affiliation matrix :

lower_level <- my_mlvsbm$adjacency_matrix$I # matrix of size nI x nI
upper_level <- my_mlvsbm$adjacency_matrix$O # matrix of size nO x nO
affiliation <- my_mlvsbm$affiliation_matrix # matrix of size nI x nO
my_mlvsbm2 <- MLVSBM::mlvsbm_create_network(X = list(I = lower_level, O = upper_level),
                                            A = affiliation)

We can now infer the parameters, blocks and edge probabilities of our network by using the mlvlsbm_estimate_network() function on an MLVSBM object. It will return the best model for this network as another R6 object of type FitMLVSBM.

fit <- MLVSBM::mlvsbm_estimate_network(my_mlvsbm, nb_cores = 1L)
#> 
#> 







[1] "Infering lower level :"
#> [1] "# blocks: 3, ICL = -1753.45290053552 !"
#> 
#> 







[1] "Infering upper level :"
#> [1] "# blocks: 3, ICL = -429.333753035444 !"
#> [1] "======= # Individual clusters : 3 , # Organisation clusters 3,  ICL : -2161.44453717678========"
#> [1] "======= # Individual blocks : 3 , # Organizational blocks 3,  ICL : -2161.44453717678========"
#> [1] "ICL for independent levels : -2182.78665357096"
#> [1] "ICL for interdependent levels : -2161.44453717678"
#> [1] "=====Interdependence is detected between the two levels!====="

Generic functions

Generic functions are provided to print, plot, extract the model parameters and predict the existence of a dyad for the fitted network.

print(fit)
#> Multilevel Stochastic Block Model -- bernoulli variant
#> =====================================================================
#> Dimension = ( 60 40 ) - ( 3 3 ) blocks.
#> =====================================================================
#> * Useful fields 
#>   $independent, $distribution, $nb_nodes, $nb_clusters, $Z 
#>   $membership, $parameters, $ICL, $vbound, $X_hat
plot(fit, type = "matrix")
#> Joining, by = c("name", "group")
#> Joining, by = "name"

coef(fit)
#> $alpha
#> $alpha$I
#>            [,1]      [,2]       [,3]
#> [1,] 0.23072817 0.5382933 0.09444543
#> [2,] 0.49659089 0.4545821 0.08339372
#> [3,] 0.09614086 0.2840693 0.13421325
#> 
#> $alpha$O
#>            [,1]       [,2]      [,3]
#> [1,] 0.66670837 0.09845986 0.0557177
#> [2,] 0.09845986 0.48056388 0.1137974
#> [3,] 0.05571770 0.11379738 0.5299402
#> 
#> 
#> $pi
#> $pi$O
#> [1] 0.1499768 0.5496535 0.3003697
#> 
#> 
#> $gamma
#>            [,1]       [,2]         [,3]
#> [1,] 0.09064248 0.19243672 8.692712e-01
#> [2,] 0.72734754 0.03843131 1.307266e-01
#> [3,] 0.18200998 0.76913196 2.105228e-06
pred <- predict(fit)

Other useful output

Output of the algorithm are stocked in the MLVSBM and FitMLVSBM objects. The MLVSBM object stocks information of the observed or simulated network and a list of all the fitted SBM and MLVSBM models.

my_mlvsbm$ICL # A data frame of the inferred models 
#>   index Q_I Q_O       ICL
#> 1     1   3   3 -2161.445
my_fit <- my_mlvsbm$fittedmodels[[which.max(my_mlvsbm$ICL$ICL)]] # The fitted model with index  the highest ICL
my_mlvsbm$ICL_sbm # The ICL of the SBM
#> $lower
#>  [1]      -Inf      -Inf -1753.453      -Inf      -Inf      -Inf      -Inf
#>  [8]      -Inf      -Inf      -Inf
#> 
#> $upper
#>  [1]      -Inf      -Inf -429.3338      -Inf      -Inf      -Inf      -Inf
#>  [8]      -Inf      -Inf      -Inf
my_sbm_lower <- my_mlvsbm$fittedmodels_sbm$lower[[3]] # A fitted SBM for the lower level with 3 blocks
my_sbm_upper <- my_mlvsbm$fittedmodels_sbm$upper[[2]] # A fitted SBM for the upper level with 2 blocks

You can also get the parameters and the clustering of the fitted model from the FitMLVSBM object as follows:

fit$parameters # The connectivity and membership parameters of the model
#> $alpha
#> $alpha$I
#>            [,1]      [,2]       [,3]
#> [1,] 0.23072817 0.5382933 0.09444543
#> [2,] 0.49659089 0.4545821 0.08339372
#> [3,] 0.09614086 0.2840693 0.13421325
#> 
#> $alpha$O
#>            [,1]       [,2]      [,3]
#> [1,] 0.66670837 0.09845986 0.0557177
#> [2,] 0.09845986 0.48056388 0.1137974
#> [3,] 0.05571770 0.11379738 0.5299402
#> 
#> 
#> $pi
#> $pi$O
#> [1] 0.1499768 0.5496535 0.3003697
#> 
#> 
#> $gamma
#>            [,1]       [,2]         [,3]
#> [1,] 0.09064248 0.19243672 8.692712e-01
#> [2,] 0.72734754 0.03843131 1.307266e-01
#> [3,] 0.18200998 0.76913196 2.105228e-06
fit$Z # The block membership of each nodes
#> $I
#>  I1  I2  I3  I4  I5  I6  I7  I8  I9 I10 I11 I12 I13 I14 I15 I16 I17 I18 I19 I20 
#>   1   3   3   1   3   2   1   2   1   3   1   3   3   1   3   3   1   1   3   1 
#> I21 I22 I23 I24 I25 I26 I27 I28 I29 I30 I31 I32 I33 I34 I35 I36 I37 I38 I39 I40 
#>   1   1   3   1   3   3   1   1   1   1   3   1   2   2   1   3   2   3   3   3 
#> I41 I42 I43 I44 I45 I46 I47 I48 I49 I50 I51 I52 I53 I54 I55 I56 I57 I58 I59 I60 
#>   2   1   1   1   2   2   1   3   3   1   2   1   3   1   2   2   2   1   3   3 
#> 
#> $O
#>  O1  O2  O3  O4  O5  O6  O7  O8  O9 O10 O11 O12 O13 O14 O15 O16 O17 O18 O19 O20 
#>   3   2   2   2   1   2   1   1   3   2   3   3   3   2   2   2   2   3   2   2 
#> O21 O22 O23 O24 O25 O26 O27 O28 O29 O30 O31 O32 O33 O34 O35 O36 O37 O38 O39 O40 
#>   2   3   2   3   2   2   1   1   1   2   2   3   2   3   2   2   3   2   2   3
fit$vbound # A vector of the varational bound of the VEM algorithm
#> [1] -2088.490 -2088.447 -2088.447 -2088.447
tau <- fit$membership # The variational parameters of the model
pred <- fit$X_hat # The links predictions for each level