W-graph refers to a general class of random graph models that can be seen as a random graph limit. It is characterized by both its graphon function and its motif frequencies. The stochastic block model is a special case of W-graph where the graphon function is block-wise constant. In this paper, we propose a variational Bayes approach to estimate the W-graph as an average of stochastic block models with increasing number of blocks. We derive a variational Bayes algorithm and the corresponding variational weights for model averaging. In the same framework, we derive the variational posterior frequency of any motif. A simulation study and an illustration on a social network complete our work.