Either kernel hackers unexpectedly love frontend, or more likely the people that write the code don't overlap much with the people that star Github projects!
They are similar because they are popular, not because there is semantic relationship.
It's the same problem I faced with the map of reddit (https://anvaka.github.io/map-of-reddit/ ) - all popular subreddits are just "similar" to each other.
Stil works great for smaller, non-celebrity projects :D
Wonder how you’d implement that in a heat map. Just call each pixel a document and see where it takes you?
A tf*idf matrix could be applied to the star-feature matrix too. Document = github repo. Term = name of user who starred it.
THUS, users who overstar are simply less important for computing similarities.
This would mitigate the phenomenon of massively popular github repos being clustered together because of folks who blithely star the most well known stuff.