In order to develop some intuition for some of the commonly used random graph models, I've been looking at the global clustering coefficient as a means of comparing them. In particular, for the general ER-Bernoulli disturbed random graph, the Watts-Strogatz random graph, and the Barabasi-Albert random graph, their global clustering distributions are as follows respectively:

For a Bernoulli graph distribution for 1000 vertices with edge probability $p$

A 1000 vertex random graph made according to Watts-Strogatz model with probability $p$

1000 vertex random graph made according to Barabasi-Albert model, where vertex deg on x-axis shows the fixed degree of each added node (until 1000 nodes is reached).

In contrast to the ER and Watts-Strogatz models, the Barabasi-Albert random graph has a power-law distributed degree distribution.

**Question:**

Could we have expected these trends in the global clustering of these models? Namely, the linear increase in the 1st one, the rapid decaying one in the 2nd and the log(?) scaling one in the last one. On the one hand, the ER-Bernoulli one is understandable since the higher the probability the more edges are added but in an independent fashion.

But on the other hand, the Watts-Strogatz model AFAIK is one that was designed such that it leads to high clustering, but why do we see a decrease in the global clustering unlike the other two models?

*With the help of choosing clustering as a factor of comparison, I'm basically trying to learn how one reasons between these 3 models.*