When using larger datasets of tree-ring series, calculating the table with similarities can take a lot of time, but finding communities even more. It is therefore recommended to use of parallel computing for Clique Percolation: clique_community_names_par(network, k=3, n_core = 4). This reduces the amount of time significantly. For most datasets clique_community_names() is sufficiently fast and for smaller datasets clique_community_names_par() can even be slower due to the parallelisation. Therefore, the funtion clique_community_names() should be used initially and if this is very slow, start using clique_community_names_par().
The workflow is similar as described in the vignette("dendroNetwork"), but with minor changes:
load network.
compute similarities.
find the maximum clique size: igraph::clique_num(network) .
detect communities for each clique size separately:
com_cpm_k3 <- clique_community_names_par(network, k=3, n_core = 6).
com_cpm_k4 <- clique_community_names_par(network, k=4, n_core = 6).
and so on until the maximum clique size.
merge these into a single data frame by com_cpm_all <- rbind(com_cpm_k3,com_cpm_k4, com_cpm_k5,... ) .
create table for use in cytoscape with all communities: com_cpm_all <- com_cpm_all |> dplyr::count(node, com_name) |> tidyr::spread(com_name, n) .
Continue with the visualisation in Cytoscape, see the relevant section in the vignette("dendroNetwork") .
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