In the paper The diameter of random regular graphs, Bollobás and Fernandez De La Vega give asymptotic lower and upper bounds on the diameter $d$ of random $r$-regular graphs. Roughly, the lower bound is $\Theta(log_{r-1}n)$, while the upper bound is $\Theta(log_{r-1}(nlogn))$, where $n$ is the number of nodes of the graph.
I would like to know (even just empirical) lower and upper bounds on the diameter of non-random $3$-regular graphs. More precisely, such non-random $3$-regular graphs in which I'm interested are those arising after reducing practical real world problem instances (which are known to be structured instead of random) to the $\sharp P$-complete problem $\sharp3$-regular Vertex Cover. In other words, suppose you have a real-world instance of a $NP$-complete problem, and suppose you want to count its solutions: following a chain of reductions, you come out with a $3$-regular graph $G$, whose number of vertex covers encodes the number of solutions of your initial instance.
Questions
- How is $G$? Is it still structured, as the original instance? Or did it destroyed the initial structure?
- If $G$ is structured, which are the lower bound and the upper bound on its diameter? Do the bounds given by Bollobás and Fernandez De La Vega still hold? Or are such bounds completely different in the structured case?
- Does there exist any automated tool (along the lines of ToughSAT) that, given an instance of a $NP$-complete problem, produces the graph $G$?