Yeah, the UX/DX of the turning these algorithms into something usable is really interesting, and something I didn't get to talk much about.
With the variations on the push algorithm, you do kind of need to know the graph topology ahead of time, at least to be able to traverse it efficiently and correctly (this is the topological sorting thing). But for pull (and therefore for push-pull), the dependencies can be completely dynamic - in a programming language, you can call `eval` or something, or in a spreadsheet you could use `indirect` to generate cell references dynamically. For push-pull specifically, when you evaluate a node you would generally delete all of its upstream connections (i.e. for each cell it depends on currently, remove that dependency) and then rebuild that node's connections to the graph while evaluating it.
Signals libraries are exactly where I found this concept, and they work basically like you describe. I think this is a big part of what makes signals work so well in comparison to, say, RxJS - they're basically the same concept (here's a new reactive primitive, let's model our data in terms of this primitive and then plug that into the UI, so we can separate business logic from rendering logic more cleanly), but the behaviour of a signal is often easier to understand because it's not built from different combinators but just described in "ordinary" code. In effect, if observables are monads that need to be wired together with the correct combinators, then signals are do-notation.