community.detection: Apply a Community Detection Algorithm

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

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,
  ...
)

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)

Value

Returns memberships from a community detection algorithm

Author(s)

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"
)

EGAnet documentation built on Nov. 18, 2023, 1:07 a.m.