Actually curious about the reasoning for it 😀

We care about some sort of threshold classification, with some sort of "pending"/leniancy state for newcomers.
Maybe even a sinkhole point-of-no-return-make-a-new-npub-bitch for spam/noted toxic individuals given your network.

 A sigmoid is standard practice for classification, but asymptotic bounds of 0 and 1 don't really help those that have been in the game for a while. So we want to classify yes and no, in between state, and also note the very (un)trustworthy individuals.

Tan and cubic curves accomplish that. They grow very fast after a certian point.

With minimal assumptions, you can just encode every data point as ±1 for positive/negative interactions, and put the average * scaling constant into the function.

Or you can weight the interactions by type (mutes>follows> sentiment classification of comments > reaction classification, or weight based on if the points are coming from your follows) and compute the weighted average.

 https://i.nostr.build/LeV86.jpg

 https://i.nostr.build/4oG23.jpg

https://i.nostr.build/VwVnj.jpg 

Coracle's WOT formula, where mutes are the argument and follows are the parameter.