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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 engagement.

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

We fit the TNA model to the data.

tna_model <- tna(engagement)
print(tna_model)
#> State Labels
#> 
#> Active, Average, Disengaged 
#> 
#> Transition Probability Matrix
#> 
#>                Active   Average Disengaged
#> Active     0.52519894 0.4279399 0.04686118
#> Average    0.24669137 0.5632610 0.19004764
#> Disengaged 0.09871795 0.4782051 0.42307692
#> 
#> Initial Probabilities
#> 
#>     Active    Average Disengaged 
#>      0.270      0.355      0.375

plot(tna_model)

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.

dyads <- cliques(tna_model, size = 2)
triads <- cliques(tna_model, size = 3)
plot(dyads)
plot(triads)

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.
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