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?
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.