I have a question related to chaper 10.2.3 ("recurrent networks as directed graphical models") of the deep learning book by Bengio et al.

In the chapter, the authors describe how one can interpret an RNN as a graphical model.

However, I still have problems understanding why we interpret RNNs as graphical models at all. What is the benefit of such an interpretation? And do we only interpret RNNs as graphical models or can we apply the idea to all kinds of neural networks?