strength_signed: Calculate signed strength statistics on a single graph.

In abnormally-distributed/rsfcNet: Analyze resting state functional connectivity networks

Description

This calculates strength statistics on a single graph.

Usage

strength_signed(graph, scale = TRUE)

Arguments

graph	A network in matrix format or as an igraph object.
scale	Defaults to TRUE to return the scaled statistic. Can be set to FALSE.

Details

Strength star is the weighted combination of the positive connections and negative connections in a network proposed by Rubinov and Sporns (2011). The statistic is calculated by the following formula:

s_i^{*}=s_i^+-≤ft(\frac{s_i^-}{s_i^++s_i^-}\right)s_i^-

where the positive and negative strength are respectively the sum of positive/negative weights:

s_i^\pm = ∑_{j\neq i}^N w_{ij}

When normalized to the -1 to 1 interval with scale=TRUE the positive and negative strength are normalized with the following formula first then plugged into the above formula:

s_i^\pm = \frac1{n-1}s_i^\pm

Value

The weighted strength metric along with the positive and negative strength.

Author(s)

Brandon Vaughan

References

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Node Degree and Strength. Chapter 4. Fundamentals of Brain Network Analysis, 115-136. doi:10.1016/B978-0-12-407908-3.00004-2

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

See Also

strength_multiple degree_centr strength degree

Examples

strength_signed(sub001_cormat, scale=TRUE)

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