|Subject:||[igraph] details for cluster_louvain local moving heuristic (r users)|
|Date:||Mon, 27 Jul 2020 08:55:14 +0100|
|User-agent:||Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:68.0) Gecko/20100101 Thunderbird/68.10.0|
I seem unable to find a detailed description of cluster_louvain for R users (e.g. https://rdrr.io/cran/igraph/src/R/community.R) besides a call to "C_R_igraph_community_multilevel" ;
I'd be interested in gaining a better understanding of how
cluster_louvain specifically deals with the local moving
heuristics (i.e. the first stage of the standard two-step
procedure as per Blondel et al. J. Stat. Mech. Theor. Exp. (2008).
Also I am aware of some useful pseudocodes, although the point of both papers is to propose an improvement to the Louvain algorithm:
Long story short, none of the above helps me unpick the cluster_louvain function specifically; nor is it meant specifically as a resource for R users. Obviously, I've attempted my own implementation in R form scratch of the 'basics' (as I understand them), albeit with mixed results: good matching for small toy examples (i.e. yields the same n. of communities and modularity score across partitions), but then it strands from cluster_louvain with slightly larger examples (not even 200 arcs...).
Grateful if anyone could point me in the right direction re
what's underneath the cluster_louvain function and whether that
might be made digestible to a C illiterate like myself.
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