[codemonkey-zeta]

I've thought about doing this before as well. One challenge you might face occurs when one algorithm goes by different names in different circles. I can't think of good examples that I ran into, but some statistical methods have one name when physicists use it, another for biologists, another for statisticians, etc.

Could be interesting to compare the similarities of the semantics of the algorithms as understood by an NLP model. E.g. depth-first-search vs. monte-carlo, or dijkstra's vs Kruskal's. Both used in similar contexts, so you could group algorithms into families. I'd love to see more NLP-driven meta-analysis of scientific literature.

[ReD_CoDE]

I couldn't agree more, and I think we can make a graph that shows parent and childs. The main algorithm and its children