Graphons and Graphs The situation is as follows: assume we have a sequence of simple weighted graphs $(G_n)_{n\in\Bbb{N}}$. For the terminology that follows I refer to Limits of dense graph sequences by László Lovász and Balázs Szegedy. Let us suppose that $(G_n)_{n}$ is convergent. The limit object, as I understand it correctly, is going to be a graphon, i.e., a symmetric measurable function $W:[0,1]^2\to[0,1]$. To such a graphon I can associate a random graph.
My question: Is it possible that a graphon $W$ gives rise to an ordinary (infinite) graph $G$ which is not a random graph, i.e., are there conditions on $W$ or the sequence $(G_n)_n$ such that I can construct one graph out of it (maybe up to isomorphisms)? If so, what are the conditions and are there any references? I hope I am clear enough about this question? Looking forward hearing from you. Best regards.
 A: Given a graphon, you can build a sequence of random graphs on $n$ vertices converging to it by sampling $n$ numbers in the interval, and connecting them independently with probability given by the graphon. You can do this construction with a countably infinite number of vertices as well.
A relevant fact is that for the constant graphons $W=p$ (for $p>0$), the random graph you get this way is the infinite Erdos-Renyi graph, also called Rado graph. These random graphs are actually not random: they are all isomorphic to each other with probability $1$ (and they also don't depend on $p$). 
It seems to me that the forward backward argument showing almost sure uniqueness of the Rado graph extends to infinite random graphs associated by general graphons, but this would need to be checked.
In any case, these infinite random (or not random actually) graphs lose a lot of information compared with the graphons generating them. This is already apparent with constant graphons, as the obtained infinite graph doesn't depend on the value $p$ of the graphon. This relates to the fact that there's no canonical probability measure on infinite sets: looking only at infinite graphs one loses the notion of density. 
