module_connectivity: Calculate several module/community based network statistics

Description Usage Arguments Details Value Author(s) References Examples

View source: R/module_connectivity.R

Description

This calculates the (within-module) strength star and diversity star metrics proposed by Rubinov and Sporns (2011) along with several other metrics.

Usage

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module_connectivity(graph, modules, n.nodes = NULL, consensus.output = FALSE)

Arguments

graph

a single network in matrix format or as an igraph object

modules

a communities object from an igraph clustering function

n.nodes

The The number of nodes in each network.

Details

This calculates the within module strength (weighted degree) z-score (using the weighted signed "strength star" metric) and "diversity star" metric proposed by Rubinov and Sporns (2011). This also returns the participation coefficient weighted by positive and negative connections in the same manner as diversity. Also returned are the percentage of positive/negative weighted degree a node has within-module out of its total weighted degree. A node that is well-integrated into a community/module should arguably have a high percentage of its total positive connections inside the module, and a relatively low percentage of its negative connections within the module.

The formula used for diversity:

h_i = -\frac{1}{log(m)} ∑_{u=1}^{N_M} \Bigg( ≤ft ( \frac{s_{iu}}{s_i} \right ) \cdot log ≤ft ( \frac{s_{iu}}{s_i} \right ) \Bigg)

The formula used for participation:

p_i = 1 - ∑_{s=1}^{N_M} ≤ft ( \frac{s_{iu}}{s_i} \right )^2

The weighting for diversity is calculated as follows (and participation the same formula):

h_{i}^{*} = h_{i}^{+} - \Bigg( \frac{s_i^{-}}{s_i^{+}+s_i^{-}}\Bigg) h_{i}^{-}

Value

A matrix containing several modularity/community structure based centrality measures.

Author(s)

Brandon Vaughan

References

Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 10.1016/j.neuron.2013.07.035

Rubinov, M., & Sporns, O. (2011). Weight-conserving characterization of complex functional brain networks. NeuroImage, 56(4), 2068-2079. doi:10.1016/j.neuroimage.2011.03.069

Examples

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## Not run: 
module_stats = module_connectivity(graph, module)

## End(Not run)

abnormally-distributed/rsfcNet documentation built on March 8, 2020, 5:32 p.m.