Sorry @warren! Looks like this may have been addressed in the github conversation. However, for those that come after, given the goals which are probably satisfied by an approximation, I don't see why I wouldn't suffice.
I think the downside is the one that I mentioned in comment. The edge weights are created using something called the observed vs. expected odds ratio:
oe_ratio = (all_questions_count * tag_count_AB) / (tag_count_A * tag_count_B)
Pulling from the github comment
This method is one way to take care of the issue where an edge or node node may be important but of low usage. For example, at a store 100 people might have a 85% probability of buying coffee and cream, but five of those people always purchase coffee, cream, and eggs. So I definitely want to keep 5 cartons of eggs in stock.
So, will five work? I think so. Yes. But I think what will technically happen is that you won't always know to keep "the eggs" (specific tag) in "stock" (on the graph), as it were, since you've presumed that you only want the top five associated "products" (tags).