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vtraag / louvain-igraph

Implementation of the Louvain algorithm for community detection with various methods for use with igraph in python.
GNU General Public License v3.0
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Matching seurat's louvain clustering #51

Closed liliblu closed 4 years ago

liliblu commented 4 years ago

I am new to graph based clustering algorithms and I was hoping you could help me out. I want to use this package to try to match the louvain clustering used in an R pacakge called seurat. They use modularity optimizer (http://www.ludowaltman.nl/slm/). They say: "the Modularity Optimizer also provides an implementation of the well-known Louvain algorithm for large-scale community detection developed by Blondel, Guillaume, Lambiotte, and Lefebvre (2008). An extension of the Louvain algorithm with a multilevel refinement procedure, as proposed by Rotta and Noack (2011), is implemented as well"

I am not sure which of the partitioners you have provided (if any) match the implementation described above, could you provide any insight? Or point me towards resources about louvain clustering methods?

I appreciate any help you can provide. Thanks for the great package!

vtraag commented 4 years ago

@liliblu sorry for the late reply!

This package provides the Louvain algorithm, as described by Blondel, Guillaume, Lambiotte, and Lefebvre (2008). The modularity optimizer from http://www.ludowaltman.nl/slm/ is an in improvement of the Louvain algorithm, which is called the Smart Local Moving algorithm. Together with the authors of that algorithm, we recently created the Leiden algorithm, which is a further improvement, being both faster and better than the original Louvain algorithm. The Leiden algorithm is available as a Python package leidenalg.

I believe you can also use the Leiden algorithm in seurat, but I am not too familiar with that package, so I can't help you with that.

liliblu commented 4 years ago

No problem! Thank you so much for the guidance, that clears it up really well. I am definitely incorporating leidenalg, it's working great! Thanks again.