Attributes {brainGraph}

Set graph, vertex, and edge attributes common in MRI analyses

Description

set_brainGraph_attr is a convenience function that sets a number of graph, vertex, and edge attributes for a given graph object. Specifically, it calculates measures that are common in MRI analyses of brain networks.

Usage

set_brainGraph_attr(g, type = c("observed", "random"),
  use.parallel = TRUE, A = NULL, xfm.type = c("1/w", "-log(w)",
  "1-w", "-log10(w/max(w))", "-log10(w/max(w)+1)"),
  clust.method = "louvain")

xfm.weights(g, xfm.type = c("1/w", "-log(w)", "1-w", "-log10(w/max(w))",
  "-log10(w/max(w)+1)"), invert = FALSE)

Arguments

g

A graph object

type

Character string indicating the type of graphs. Default: observed

use.parallel

Logical indicating whether to use foreach. Default: TRUE

A

Numeric matrix; the (weighted) adjacency matrix, which can be used for faster calculation of local efficiency. Default: NULL

xfm.type

Character string specifying how to transform the weights. Default: 1/w

clust.method

Character string indicating which method to use for community detection. Default: 'louvain'

invert

Logical indicating whether or not to invert the transformation. Default: FALSE

Details

Including type='random' in the function call will reduce the number of attributes calculated. It will only add graph-level attributes for: clustering coefficient, characteristic path length, rich club coefficient, global efficiency, and modularity.

Value

A graph object with the following attributes:

Graph-level

Density, connected component sizes, diameter, # of triangles, transitivity, average path length, assortativity, global & local efficiency, modularity, vulnerability, hub score, rich-club coefficient, # of hubs, edge asymmetry

Vertex-level

Degree, strength; betweenness, eigenvector, and leverage centralities; hubs; transitivity (local); k-core, s-core; local & nodal efficiency; color (community, lobe, component); membership (community, lobe, component); gateway and participation coefficients, within-module degree z-score; vulnerability; and coordinates (x, y, and z)

Edge-level

Color (community, lobe, component), edge betweenness, Euclidean distance (in mm), weight (if weighted)

xfm.weights returns the same graph object, with transformed edge weights plus a graph attribute (xfm.type) recording the method of transformation

Negative edge weights

If there are any negative edge weights in the graph, several of the distance-based metrics will not be calculated, because they can throw errors which is undesirable when processing a large dataset. The metrics are: local and nodal efficiency, diameter, characteristic path length, and hubness.

Transforming edge weights

For distance-based measures, it is important to transform the edge weights so that the strongest connections are re-mapped to having the lowest weights. Then you may calculate e.g., the shortest path length which will include the strongest connections.

xfm.type allows you to choose from 5 options for transforming edge weights when calculating distance-based metrics (e.g., shortest paths). There is no “best-practice” for choosing one over the other, but the reciprocal is probably most common.

1/w

reciprocal (default)

-log(w)

the negative (natural) logarithm

1-w

subtract weights from 1

-log10(w/max(w))

negative (base-10) log of normalized weights

-log10(w/max(w)+1)

same as above, but add 1 before taking the log

To transform the weights back to original values, specify invert=TRUE.

Community detection

clust.method allows you to choose from any of the clustering (community detection) functions available in igraph. These functions begin with cluster_; the function argument should not include this leading character string. There are a few possibilities, depending on the value and the type of input graph:

1. By default, louvain is used, calling cluster_louvain

2. Uses spinglass if there are any negative edges and/or the selected method is spinglass

3. Uses walktrap if there are any negative edge weights and any other method (besides spinglass) is selected

4. Automatically transforms the edge weights if edge_betweenness is selected and the graph is weighted, because the algorithm considers edges as distances

Author(s)

Christopher G. Watson, [e-mail]

See Also

components, diameter, centr_betw, betweenness, centr_eigen, transitivity, distances, assortativity, coreness, communities, knn