Applies the consensus clustering method introduced by (Lancichinetti & Fortunato, 2012). The original implementation of this method applies a community detection algorithm repeatedly to the same network. With stochastic networks, the algorithm is likely to identify different community solutions with many repeated applications.
community.consensus(
network,
order = c("lower", "higher"),
resolution = 1,
consensus.method = c("highest_modularity", "iterative", "most_common", "lowest_tefi"),
consensus.iter = 1000,
correlation.matrix = NULL,
allow.singleton = FALSE,
membership.only = TRUE,
...
)Returns either a vector with the selected solution
or a list when membership.only = FALSE:
Resulting solution from the consensus method
Matrix of memberships across the consensus iterations
For methods that use frequency, a table that reports those frequencies alongside their corresponding memberships
Matrix or igraph network object
Character (length = 1).
Defaults to "higher".
Whether "lower" or "higher" order memberships from
the Louvain algorithm should be obtained for the consensus.
The "lower" order Louvain memberships are from the first
initial pass of the Louvain algorithm whereas the "higher"
order Louvain memberships are from the last pass of the Louvain
algorithm
Numeric (length = 1).
A parameter that adjusts modularity to allow the algorithm to
prefer smaller (resolution > 1) or larger
(0 < resolution < 1) communities.
Defaults to 1 (standard modularity computation)
Character (length = 1).
Defaults to "most_common".
Available options for arriving at a consensus (Note:
All methods except "iterative" are considered experimental
until validated):
"highest_modularity" --- EXPERIMENTAL. Selects the community solution with the highest modularity across
the applications. Modularity is a reasonable metric for identifying the number
of communities in a network but it comes with limitations (e.g., resolution limit)
"iterative" --- The original approach proposed by Lancichinetti & Fortunato (2012). See
"Details" for more information
"most_common" --- Selects the community solution that appears the most
frequently across the applications. The idea behind this method is that the solution
that appears most often will be the most likely solution for the algorithm as well
as most reproducible. Can be less stable as the number of nodes increase requiring
a larger value for consensus.iter. This method is the default
"lowest_tefi" --- EXPERIMENTAL. Selects the community solution with the lowest Total Entropy
Fit Index (tefi) across the applications. TEFI is a reasonable metric
to identify the number of communities in a network based on Golino, Moulder et al. (2020)
Numeric (length = 1).
Number of algorithm applications to the network.
Defaults to 1000
Symmetric matrix.
Used for computation of tefi.
Only needed when consensus.method = "tefi"
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
Boolean.
Whether the memberships only should be output.
Defaults to TRUE.
Set to FALSE to obtain all output for the
community detection algorithm
Not actually used but makes it easier for general functionality in the package
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
The goal of the consensus clustering method is to identify a stable solution across algorithm applications to derive a "consensus" clustering. The standard or "iterative" approach is to apply the community detection algorithm N times. Then, a co-occurrence matrix is created representing how often each pair of nodes co-occurred across the applications. Based on some cut-off value (e.g., 0.30), co-occurrences below this value are set to zero, forming a "new" sparse network. The procedure proceeds until all nodes co-occur with all other nodes in their community (or a proportion of 1.00).
Variations of this procedure are also available in this package but are experimental. Use these experimental procedures with caution. More work is necessary before these experimental procedures are validated
At this time, seed setting for consensus clustering is not supported
Louvain algorithm
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008).
Fast unfolding of communities in large networks.
Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Consensus clustering
Lancichinetti, A., & Fortunato, S. (2012).
Consensus clustering in complex networks.
Scientific Reports, 2(1), 1–7.
Entropy fit indices
Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020).
Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables.
Multivariate Behavioral Research.
# Load data
wmt <- wmt2[,7:24]
# Estimate correlation matrix
correlation.matrix <- auto.correlate(wmt)
# Estimate network
network <- EBICglasso.qgraph(data = wmt)
# Compute standard Louvain with highest modularity approach
community.consensus(
network,
consensus.method = "highest_modularity"
)
# Compute standard Louvain with iterative (original) approach
community.consensus(
network,
consensus.method = "iterative"
)
# Compute standard Louvain with most common approach
community.consensus(
network,
consensus.method = "most_common"
)
# Compute standard Louvain with lowest TEFI approach
community.consensus(
network,
consensus.method = "lowest_tefi",
correlation.matrix = correlation.matrix
)
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