normalizing Bayesian Network score

I have been doing data driven causal inference from Bayesian Networks in R using bnlearn package on a random categorical dataset. The data is used to learn the structure and parameters of model. Below is the code for learning structure of Bayesian network.

boot_hc <- boot.strength(data = df, R = 100, m = nrow(df), algorithm = "hc", algorithm.args = list(score="bic"), cpdag = F, debug = T)

bn_hc <- averaged.network(strength = boot_hc, threshold = 0.85)

The image below represents the learned structure

The BIC score for the above DAG is -339005.6 computed using

> score(x = bn, data = df, type = "bic") [1] -339005.6

My question is

How can I normalize the BIC score (or any other score function) in order to get more understanding of quality of fit of learned structure ?