This function sets a number of graph, vertex, and edge attributes for a
given igraph graph object. These are all measures that are common in
MRI analyses of brain networks.
set_brainGraph_attr(g, atlas = NULL, rand = FALSE, use.parallel = TRUE,
A = NULL, xfm.type = c("1/w", "-log(w)", "1-w"), ...)An igraph graph object
Character vector indicating which atlas was used (default:
NULL)
Logical indicating if the graph is random or not (default:
FALSE)
Logical indicating whether or not to use foreach
(default: TRUE)
Numeric matrix; the (weighted) adjacency matrix, which can be used
for faster calculation of local efficiency (default: NULL)
Character string indicating how to transform edge weights
(default: 1/w [reciprocal])
Other arguments passed to make_brainGraph
g An igraph graph object with the following attributes:
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, and modality
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)
Color (community, lobe, component), edge betweenness, Euclidean distance (in mm), weight (if weighted)
xfm.type allows you to choose from 3 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
components, diameter,
clique_num, centr_betw, part_coeff,
edge.betweenness, centr_eigen,
gateway_coeff, hub.score,
authority.score, transitivity,
mean_distance, assortativity_degree,
assortativity_nominal,
cluster_louvain, efficiency,
set_edge_color, rich_club_coeff, s_core,
within_module_deg_z_score, coreness,
edge_spatial_dist, vulnerability, centr_lev,
edge_asymmetry, graph.knn, vertex_spatial_dist