Main clustering function for condor. — condorCluster • netZooR

This function performs community structure clustering using the bipartite modularity described in condorModularityMax. This function uses a standard (non-bipartite) community structure clustering of the uni-partite, weighted projection of the original bipartite graph as an initial guess for the bipartite modularity.

Usage

condorCluster(
  condor.object,
  cs.method = "LCS",
  project = TRUE,
  low.memory = FALSE,
  deltaQmin = "default"
)

Arguments

condor.object: Output of make.condor.object. This function uses condor.object$edges
cs.method: is a string to specify which unipartite community structure algorithm should be used for the seed clustering. Options are LCS (multilevel.community), LEC (leading.eigenvector.community), FG (fastgreedy.community).
project: Provides options for initial seeding of the bipartite modularity maximization. If TRUE, the nodes in the first column of condor.object$edges are projected and clustered using cs.method. If FALSE, the complete bipartite network is clustered using the unipartite clustering methods listed in cs.method.
low.memory: If TRUE, uses condorModularityMax instead of condorMatrixModularity. This is a slower implementation of the modularity maximization, which does not store any matrices in memory. Useful on a machine with low RAM. However, runtimes are (much) longer.
deltaQmin: convergence parameter determining the minimum required increase in the modularity for each iteration. Default is min(10^-4,1/(number of edges)), with number of edges determined by nrow(condor.object$edges). User can set this parameter by passing a numeric value to deltaQmin.

Value

condor.object with condorModularityMax output included.

Examples

r = c(1,1,1,2,2,2,3,3,3,4,4);
b = c(1,2,3,1,2,4,2,3,4,3,4);
reds <- c("Alice","Sue","Janine","Mary")
blues <- c("Bob","John","Ed","Hank")
elist <- data.frame(red=reds[r],blue=blues[b])
condor.object <- createCondorObject(elist)
condor.object <- condorCluster(condor.object)