This is a great name!! For those who aren't familiar, there's a famous graph data set about a karate club: https://en.wikipedia.org/wiki/Zachary%27s_karate_club

Thanks for bringing me back to undergrad... :) (...where I learned about this data set.)

Thank you for sharing. The DOI is 10.2307/3629752 if anyone wants to look it up.

+1 on the great name and thanks for the reference link

Very cool. This kind of work makes python and ML better for everyone. I'm not sure I have a use case at the moment, but I'll definitely be keeping an eye on this one.

I've recently been looking into community detection for NetworX graphs for my collection of notes. How does performance compare with other community detection packages for NetworkX like Python-Louvain?

Random side note: nowadays 'Leiden' is a recommended improvement on 'Louvain', fixing some connectivity issues and being faster: https://www.nature.com/articles/s41598-019-41695-z https://github.com/vtraag/leidenalg

Performance measured by what? NMI? Modularity?

Not super familiar with the measures, I'm just curious how it compares to other available approaches

Louvain greedily optimizes modularity - it is hard to beat it in terms of modularity.