Finding cliques and communities with tna

The tna package includes functionalities for finding cliques of the transition network as well as discovering communities. We begin by loading the package and the example data set group_regulation.

library("tna")
data("group_regulation", package = "tna")

tna_model <- tna(group_regulation)
print(tna_model)
#> State Labels : 
#> 
#>    adapt, cohesion, consensus, coregulate, discuss, emotion, monitor, plan, synthesis 
#> 
#> Transition Probability Matrix :
#> 
#>                   adapt   cohesion  consensus coregulate    discuss    emotion
#> adapt      0.0000000000 0.27308448 0.47740668 0.02161100 0.05893910 0.11984283
#> cohesion   0.0029498525 0.02713864 0.49793510 0.11917404 0.05958702 0.11563422
#> consensus  0.0047400853 0.01485227 0.08200348 0.18770738 0.18802338 0.07268131
#> coregulate 0.0162436548 0.03604061 0.13451777 0.02335025 0.27360406 0.17208122
#> discuss    0.0713743356 0.04758289 0.32118451 0.08428246 0.19488737 0.10579600
#> emotion    0.0024673951 0.32534367 0.32040888 0.03419105 0.10186817 0.07684173
#> monitor    0.0111653873 0.05582694 0.15910677 0.05792045 0.37543615 0.09071877
#> plan       0.0009745006 0.02517460 0.29040117 0.01721618 0.06789021 0.14682475
#> synthesis  0.2346625767 0.03374233 0.46625767 0.04447853 0.06288344 0.07055215
#>               monitor       plan   synthesis
#> adapt      0.03339882 0.01571709 0.000000000
#> cohesion   0.03303835 0.14100295 0.003539823
#> consensus  0.04661084 0.39579712 0.007584137
#> coregulate 0.08629442 0.23908629 0.018781726
#> discuss    0.02227284 0.01164262 0.140976968
#> emotion    0.03630596 0.09975326 0.002819880
#> monitor    0.01814375 0.21563154 0.016050244
#> plan       0.07552379 0.37420822 0.001786584
#> synthesis  0.01226994 0.07515337 0.000000000
#> 
#> Initial Probabilities : 
#> 
#>      adapt   cohesion  consensus coregulate    discuss    emotion    monitor 
#>     0.0115     0.0605     0.2140     0.0190     0.1755     0.1515     0.1440 
#>       plan  synthesis 
#>     0.2045     0.0195
plot(tna_model)
#> Registered S3 method overwritten by 'cograph':
#>   method             from
#>   plot.tna_bootstrap tna

Next, we apply several community finding algorithms to the model (see ?communities for more details), and plot the results for the leading_eigen algorithm.

cd <- communities(tna_model)
plot(cd, method = "leading_eigen")

Cliques can be obtained with the cliques function. Here we look for dyads and triads by setting size = 2 and size = 3, respectively. Finally, we plot the results.

layout(matrix(1:4, ncol = 2, byrow = TRUE))
dyads <- cliques(tna_model, size = 2, threshold = 0.2)
triads <- cliques(tna_model, size = 3, threshold = 0.05)
plot(dyads, ask = FALSE)
plot(triads, ask = FALSE)

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Finding cliques and communities with tna