SMILES vs. Graph for generating molecules

I have seen [this question](https://chemistry.stackexchange.com/questions/128149/smiles-vs-graph-representation-in-deep-learning?newreg=475762532b0b4b7c96489aaa1c4c7677), but mine is slightly different, focusing on *generating* molecules.


There are many published methods for generating molecules; some of which generate SMILES and others directly generate graphs. Comparing these two approaches, I came to the following conclusions:


* These two structures can be converted to each other. Obviously, we can obtaion SMILES from graphs by graph traversal. On the other hand, one can generate graph from SMILES using `MolFromSmiles` function in rdkit. One can say that the graph representation may contain more information represented in their nodes/edges feature vectors, but I assume that current generators just generate graph structures.
* The main merit of directly generating graphs over SMILES is that we hope that the former generates better molecules. This is because, as mentioned in [MolGAN paper](https://arxiv.org/abs/1805.11973), models that generate SMILES have to spend capacity on learning both the syntactic rules and the order ambiguity of the representation.


I appreciate if you can "validate" or "correct" the above conclusions.


