Goldman took the same approach with instrument pricing ~30 years ago. I recall long discussions about "Node Purpling" in my ~13 year tenure there.
Computer Science has evolved, and AFAICT this is not a graph approach, but things like differentiation are computationally expensive, and therefore you want to minimize the number of times you do it to as close to the theoretical minimum.
Edit: Related HN discussion https://news.ycombinator.com/item?id=36006737
Yea and this created "bank python" informally a good article is here.
https://calpaterson.com/bank-python.html
The best description about how it became a problem is one of the paragraphs.
"New starters take an exceptionally long time to get up to speed - and that's if they don't resign in fit of pique as soon as they see the special, mandatory, in-house IDE (as I nearly did). Even months in, new starters are still learning quite fundamental new things: there is a lot that is different."
I think it took me til I was there around two and a half years to fully comprehend it when I was working on it. Not much modern training til they figured out they had to teach it again that was better. The worst part is to make an UI around it coding it and it wasn't approved for new projects.