This is a cool example of how specializing a generic algorithm to a specific subspace can yield much better results. This is quite often the case in my experience, but we often don't bother utilizing properties that are specific to our problem space, and just apply the generic algorithm out of convenience (and because it is often good enough)
I wrote my thesis on this! Application-specific system design can get you orders of magnitude performance improvement, as well as better scalability/fault tolerance properties. I focused on graph analytics, but it's reasonable to think it applies more broadly.
Definitely true that application-specific design is often not worth the investment though. Chasing that 1000x improvement can easily cost you a year or two.
Can you please link to your thesis? This sounds very interesting.
Came here to say this, but with caveats. The particular domain has extra properties that allow their "stupider" algorithm to work better in their case. But a general graph drawing system has to deal with the inherent generality of the domain.
Usually there is a good middle ground: heuristic analysis of the input to see if it fits well with special-case "stupid and fast" algorithms, and sophisticated optimizations that are the fallback and work for everything and come with guarantees.
That's how all application-specific specializations work though, take advantage of domain properties that make you need a less generic algorithm.