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netdiffuseR (version 1.17.0)

dgr: Indegree, outdegree and degree of the vertices

Description

Computes the requested degree measure for each node in the graph.

Usage

dgr(graph, cmode = "degree", undirected = getOption("diffnet.undirected", FALSE), self = getOption("diffnet.self", FALSE), valued = getOption("diffnet.valued", FALSE))
"plot"(x, breaks = min(100L, nrow(x)/5), freq = FALSE, y = NULL, log = "xy", hist.args = list(), slice = ncol(x), xlab = "Degree", ylab = "Freq", ...)

Arguments

graph
Any class of accepted graph format (see netdiffuseR-graphs).
cmode
Character scalar. Either "indegree", "outdegree" or "degree".
undirected
Logical scalar. When TRUE only the lower triangle will be processed.
self
Logical scalar. When TRUE allows loops (self edges).
valued
Logical scalar. When TRUE weights will be considered. Otherwise non-zero values will be replaced by ones.
x
An diffnet_degSeq object
breaks
Passed to hist.
freq
Logical scalar. When TRUE the y-axis will reflex counts, otherwise densities.
y
Ignored
log
Passed to plot (see par).
hist.args
Arguments passed to hist.
slice
Integer scalar. In the case of dynamic graphs, number of time point to plot.
xlab
Character scalar. Passed to plot.
ylab
Character scalar. Passed to plot.
...
Further arguments passed to plot.

Value

A numeric matrix of size $n * T$. In the case of plot, returns an object of class histogram.

See Also

Other statistics: classify_adopters, cumulative_adopt_count, ego_variance, exposure, hazard_rate, infection, moran, struct_equiv, threshold, vertex_covariate_dist

Other visualizations: diffusionMap, drawColorKey, grid_distribution, hazard_rate, plot_adopters, plot_diffnet2, plot_diffnet, plot_infectsuscep, plot_threshold, rescale_vertex_igraph

Examples

Run this code

# Comparing degree measurements ---------------------------------------------
# Creating an undirected graph
graph <- rgraph_ba()
graph

data.frame(
   In=dgr(graph, "indegree", undirected = FALSE),
   Out=dgr(graph, "outdegree", undirected = FALSE),
   Degree=dgr(graph, "degree", undirected = FALSE)
 )

# Testing on Korean Family Planning (weighted graph) ------------------------
data(kfamilyDiffNet)
d_unvalued <- dgr(kfamilyDiffNet, valued=FALSE)
d_valued   <- dgr(kfamilyDiffNet, valued=TRUE)

any(d_valued!=d_unvalued)

# Classic Scale-free plot ---------------------------------------------------
set.seed(1122)
g <- rgraph_ba(t=1e3-1)
hist(dgr(g))

# Since by default uses logscale, here we suppress the warnings
# on points been discarded for <=0.
suppressWarnings(plot(dgr(g)))

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