A couple of other thoughts with this perspective: - if a drawing of a skiplist is 2D, HNSW seems to be a 3D generalization. It makes me wonder if you could apply an HNSW-like scheme for a traditional relational database index (maybe a multicolumn index?). - if a skiplist is a probabilistic alternative to a B+-tree, what's HNSW a probabilistic alternative to?
One thing that still mystifies me is how in the context of vector indexing, the HNSW seems to "bootstrap" itself by using itself to find the neighbors of a newly-inserted point. It's not clear to me why that doesn't diverge to some garbage results as the index grows.
I don't think that analogy is accurate.
A prolly tree could also be considered a probabilistic version of a b-tree and a skiplist could be considered a probabilistic version of a trie, so you certainly have probabilistic vs. non-probabilistic datastructures, but there isn't really a 1-to-1 correspondence, so your inference of a missing X doesn't make that much sense.
Here's a better dichotomy imho. Both of these probabilistic and non-probabilistic datastructures share a commonality. They operate on totally ordered data.
So a skiplist is actually 1D.
HNSW and other approximate k-nearesr neighbour algorithms however operate over metric spaces, where elements have a distance/similiarity but no meaningful order.[1]
Now if you ask for the deterministic equivalent of a approximate-kNN algorithm/datastructure, then that would simply be anything in the kNN category.
1: https://math.stackexchange.com/questions/1429373/why-is-ther...
This nifty feature leads to the need to scale up. For example, given many vectors in a database which one is most similar to my word or image. Applications like this need ways to efficiently scale these kinds of searches and HNSW is one prominent technique to enable this.