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")
.