EGAnet (version 2.0.7)

community.detection: Apply a Community Detection Algorithm

Description

General function to apply community detection algorithms available in igraph. Follows the EGAnet approach of setting singleton and disconnected nodes to missing (NA)

Usage

community.detection(
  network,
  algorithm = c("edge_betweenness", "fast_greedy", "fluid", "infomap", "label_prop",
    "leading_eigen", "leiden", "louvain", "optimal", "spinglass", "walktrap"),
  allow.singleton = FALSE,
  membership.only = TRUE,
  ...
)

Value

Returns memberships from a community detection algorithm

Arguments

network

Matrix or igraph network object

algorithm

Character or igraph cluster_* function (length = 1). Available options:

• "edge_betweenness" --- See cluster_edge_betweenness for more details

• "fast_greedy" --- See cluster_fast_greedy for more details

• "fluid" --- See cluster_fluid_communities for more details

• "infomap" --- See cluster_infomap for more details

• "label_prop" --- See cluster_label_prop for more details

• "leading_eigen" --- See cluster_leading_eigen for more details

• "leiden" --- See cluster_leiden for more details. Note: The Leiden algorithm will default to the modularity objective function (objective_function = "modularity"). Set objective_function = "CPM" to use the Constant Potts Model instead (see examples)

• "louvain" --- See cluster_louvain for more details

• "optimal" --- See cluster_optimal for more details

• "spinglass" --- See cluster_spinglass for more details

• "walktrap" --- See cluster_walktrap for more details

allow.singleton

Boolean (length = 1). Whether singleton or single node communities should be allowed. Defaults to FALSE. When FALSE, singleton communities will be set to missing (NA); otherwise, when TRUE, singleton communities will be allowed

membership.only

Boolean (length = 1). Whether the memberships only should be output. Defaults to TRUE. Set to FALSE to obtain all output for the community detection algorithm

...

Additional arguments to be passed on to igraph's community detection functions (see algorithm for link to arguments of each algorithm)

Author

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <[e-mail]>

References

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.

Examples

# Load data
wmt <- wmt2[,7:24]

# Estimate network
network <- EBICglasso.qgraph(data = wmt)

# Compute Edge Betweenness
community.detection(network, algorithm = "edge_betweenness")

# Compute Fast Greedy
community.detection(network, algorithm = "fast_greedy")

# Compute Fluid
community.detection(
  network, algorithm = "fluid",
  no.of.communities = 2 # needs to be set
)

# Compute Infomap
community.detection(network, algorithm = "infomap")

# Compute Label Propagation
community.detection(network, algorithm = "label_prop")

# Compute Leading Eigenvector
community.detection(network, algorithm = "leading_eigen")

# Compute Leiden (with modularity)
community.detection(
  network, algorithm = "leiden",
  objective_function = "modularity"
)

# Compute Leiden (with CPM)
community.detection(
  network, algorithm = "leiden",
  objective_function = "CPM",
  resolution_parameter = 0.05 # "edge density"
)

# Compute Louvain
community.detection(network, algorithm = "louvain")

# Compute Optimal (identifies maximum modularity solution)
community.detection(network, algorithm = "optimal")

# Compute Spinglass
community.detection(network, algorithm = "spinglass")

# Compute Walktrap
community.detection(network, algorithm = "walktrap")

# Example with {igraph} network
community.detection(
  convert2igraph(network), algorithm = "walktrap"
)