```
library(nett)
library(Matrix)
```

Let us create a simple degree-corrected planted parition (DCPP) model:

```
set.seed(1)
= 1000 # the number of nodes
n = 4 # the number of true communties
Ktru = 15 # the average degree
lambda = 0.1 # the out-in ratio
oir = rep(1,Ktru)/Ktru
pri <- EnvStats::rpareto(n, 2/3, 3) # node conn. propensity parameter
theta
= pp_conn(n, oir, lambda, pri, theta)$B # create connectivity matrix
B = sample(Ktru, n, replace=T, prob=pri) # sample node labels
z = sample_dcsbm(z, B, theta) # sample the adjacency matrix A
```

Check to see if the average degree matches the target:

```
mean(rowSums(A))
#> [1] 15.118
```

Compute and plot the community profile based on SNAC+ test statistic:

```
= snac_resample(A, nrep = 10, ncores = 1)
tstat #> Warning: did not converge in 10 iterations
plot_smooth_profile(tstat, "A DCPP network")
```

See the article Adjusted chi-square test for degree-corrected block models, Zhang and Amini for how these profiles are constructed.