That is a great observation @ernest! And it’s something we’ve been thinking about as well.
When you say you end up with a ‘messy’ graph, do you mean aesthetically or does it have performance implications as well? In the experiment described in this post, I could very quickly see that extreme denormalisation – while ensuring you cater for the large majority of use cases – results in a graph whose visual representation is unintelligible. That is why there are not a lot of pictures in this post; and in the one that I included, I had to dramatically cut down the number of represented nodes.
But I wonder: is that messiness superficial or does it have implications for the end analysis.
We’ve definitely considered more narrow use cases, with prepped data; and future posts will expand on those.