Preliminaries
This vignette illustrates the use of the
estimateMultiplexSBM function and the methods accompanying
the R6 classes multiplexSBMfit on the war data
set.
Requirements
The packages required for the analysis are sbm plus
some others for data manipulation and representation:
library(sbm)
library(igraph)
library(aricode)
Data set
The war data set comes in the sbm
package:
This data set contains a list of two networks
(belligerent and alliance) where the nodes are
countries; an edge in the network belligerent means that
the two countries have been at war at least once between years 1816 to
2007; an edge in network alliance means that the two
countries have had a formal alliance between years 1816 and 2012. The
network belligerent have less nodes since countries which
have not been at war at all are not considered.
These two networks were extracted from https://correlatesofwar.org/
(see Sarkees and Wayman (2010) for war data, and Gibler (2008) for formal
alliance). Version 4.0 was used for war data and version 4.1 for formal
alliance.
Data manipulation
Since they don’t have the same size, we choose to only consider nodes
(countries) which were at war with at least one other country. This
corresponds to the first 83 nodes in the Alliance network.
A <- as.matrix(get.adjacency(war$alliance))
#> Warning: `get.adjacency()` was deprecated in igraph 2.0.0.
#> ℹ Please use `as_adjacency_matrix()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> This graph was created by an old(er) igraph version.
#> Call upgrade_graph() on it to use with the current igraph version
#> For now we convert it on the fly...
A <- A[1:83, 1:83]
B <- as.matrix(get.adjacency(war$belligerent))
#> This graph was created by an old(er) igraph version.
#> Call upgrade_graph() on it to use with the current igraph version
#> For now we convert it on the fly...
We can start with a plot of this multiplex network:
netA <- defineSBM(A, model = "bernoulli", dimLabels = "country")
netB <- defineSBM(B, model = "bernoulli", dimLabels = "country")
plotMyMultiplexMatrix(list(netA, netB))
Fitting a multiplex SBM model where the two layers are assumed to be
independent
We run the estimation of this multiplex model. By setting
dependent=FALSE, we declare that we consider the two layers
to be independent conditionally on the latent block variables.
MultiplexFitIndep <- estimateMultiplexSBM(list(netA, netB), dependent = FALSE,
estimOptions = list(verbosity = 0))
We can retrieve the clustering
clust_country_indep <- MultiplexFitIndep$memberships[[1]]
sort(clust_country_indep)
#> United States of America Canada
#> 1 1
#> Belize El Salvador
#> 2 2
#> Colombia Ecuador
#> 2 2
#> Bolivia Uruguay
#> 2 2
#> Bahamas Cuba
#> 3 3
#> Haiti Dominican Republic
#> 3 3
#> Jamaica Trinidad and Tobago
#> 3 3
#> Barbados Dominica
#> 3 3
#> Grenada St. Lucia
#> 3 3
#> St. Vincent and the Grenadines Antigua & Barbuda
#> 3 3
#> St. Kitts and Nevis Mexico
#> 3 3
#> Guatemala Honduras
#> 3 3
#> Nicaragua Costa Rica
#> 3 3
#> Panama Venezuela
#> 3 3
#> Guyana Suriname
#> 3 3
#> Peru Brazil
#> 3 3
#> Paraguay Chile
#> 3 3
#> Argentina United Kingdom
#> 3 4
#> Netherlands Belgium
#> 4 4
#> Luxembourg France
#> 4 4
#> Spain Portugal
#> 4 4
#> Germany German Federal Republic
#> 4 4
#> Poland Hungary
#> 4 4
#> Czech Republic Italy
#> 4 4
#> Greece Norway
#> 4 4
#> Denmark Iceland
#> 4 4
#> Bavaria German Democratic Republic
#> 5 5
#> Baden Wuerttemburg
#> 5 5
#> Austria-Hungary Austria
#> 5 5
#> Czechoslovakia Slovakia
#> 5 5
#> Malta Albania
#> 5 5
#> Croatia Yugoslavia
#> 5 5
#> Bosnia and Herzegovina Cyprus
#> 5 5
#> Bulgaria Moldova
#> 5 5
#> Romania Russia
#> 5 5
#> Estonia Latvia
#> 5 5
#> Lithuania Ukraine
#> 5 5
#> Belarus Armenia
#> 5 5
#> Georgia Azerbaijan
#> 5 5
#> Finland Sweden
#> 5 5
#> Cape Verde Sao Tome and Principe
#> 5 5
#> Guinea-Bissau
#> 5
And we can plot the reorganized adjacency matrices or the
corresponding expectations:
plot(MultiplexFitIndep, type = "expected")
Fitting a multiplex SBM model where the two layers are assumed to be
dependent
Now we assume that the two layers are dependent conditionally to the
latent block variables. We then set dependent = TRUE
MultiplexFitdep <- estimateMultiplexSBM(list(netA, netB), dependent = TRUE,
estimOptions = list(verbosity = 0))
We can retrieve the clustering and compare it to the one obtained in
the independent case.
clust_country_dep <- MultiplexFitdep$memberships[[1]]
sort(clust_country_indep)
#> United States of America Canada
#> 1 1
#> Belize El Salvador
#> 2 2
#> Colombia Ecuador
#> 2 2
#> Bolivia Uruguay
#> 2 2
#> Bahamas Cuba
#> 3 3
#> Haiti Dominican Republic
#> 3 3
#> Jamaica Trinidad and Tobago
#> 3 3
#> Barbados Dominica
#> 3 3
#> Grenada St. Lucia
#> 3 3
#> St. Vincent and the Grenadines Antigua & Barbuda
#> 3 3
#> St. Kitts and Nevis Mexico
#> 3 3
#> Guatemala Honduras
#> 3 3
#> Nicaragua Costa Rica
#> 3 3
#> Panama Venezuela
#> 3 3
#> Guyana Suriname
#> 3 3
#> Peru Brazil
#> 3 3
#> Paraguay Chile
#> 3 3
#> Argentina United Kingdom
#> 3 4
#> Netherlands Belgium
#> 4 4
#> Luxembourg France
#> 4 4
#> Spain Portugal
#> 4 4
#> Germany German Federal Republic
#> 4 4
#> Poland Hungary
#> 4 4
#> Czech Republic Italy
#> 4 4
#> Greece Norway
#> 4 4
#> Denmark Iceland
#> 4 4
#> Bavaria German Democratic Republic
#> 5 5
#> Baden Wuerttemburg
#> 5 5
#> Austria-Hungary Austria
#> 5 5
#> Czechoslovakia Slovakia
#> 5 5
#> Malta Albania
#> 5 5
#> Croatia Yugoslavia
#> 5 5
#> Bosnia and Herzegovina Cyprus
#> 5 5
#> Bulgaria Moldova
#> 5 5
#> Romania Russia
#> 5 5
#> Estonia Latvia
#> 5 5
#> Lithuania Ukraine
#> 5 5
#> Belarus Armenia
#> 5 5
#> Georgia Azerbaijan
#> 5 5
#> Finland Sweden
#> 5 5
#> Cape Verde Sao Tome and Principe
#> 5 5
#> Guinea-Bissau
#> 5
aricode::ARI(clust_country_indep, clust_country_dep)
#> [1] 0.8886699
On top of the clustering comparison, we can compare the ICL criterion
to see which of the dependent or independent models is a best fit:
MultiplexFitdep$ICL
#> [1] -1470.276
MultiplexFitIndep$ICL
#> [1] -1434.602
We can do the same plots for the reorganized matrices and the
corresponding expectations. Note that the expectations correspond to the
marginal expectation of each layer and it may be relevant to have a look
at the conditional expectations.
plot(MultiplexFitdep, type = "expected")
#> Warning in self$predict(): provided expectations are the marginal expectations
#> in the dependent case
For instance, we may want to compare the marginal distribution for
two countries in their given blocks to have being at war while they are
allied at some point (before or after):
p11 <- MultiplexFitdep$connectParam$prob11
p01 <- MultiplexFitdep$connectParam$prob01
p10 <- MultiplexFitdep$connectParam$prob10
# conditional probabilities of being at war while having been or will
# be allied
round(p11/(p11 + p10), 2)
#> [,1] [,2] [,3] [,4]
#> [1,] 0.09 0.10 0.03 0.43
#> [2,] 0.10 0.11 0.09 0.14
#> [3,] 0.03 0.09 0.05 0.03
#> [4,] 0.43 0.14 0.03 0.01
# marginal probabilities of being at war
round(p11 + p01, 2)
#> [,1] [,2] [,3] [,4]
#> [1,] 0.07 0.04 0.03 0.14
#> [2,] 0.04 0.10 0.04 0.13
#> [3,] 0.03 0.04 0.05 0.03
#> [4,] 0.14 0.13 0.03 0.02
References
Gibler, Douglas M. 2008. International Military Alliances,
1648-2008. CQ Press.
Sarkees, Meredith Reid, and Frank Whelon Wayman. 2010. Resort to
War: A Data Guide to Inter-State, Extra-State, Intra-State, and
Non-State Wars, 1816-2007. Cq Pr.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.