Indegree, outdegree and degree of the vertices

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

dgr(
  graph,
  cmode = "degree",
  undirected = getOption("diffnet.undirected", FALSE),
  self = getOption("diffnet.self", FALSE),
  valued = getOption("diffnet.valued", FALSE)
)

# S3 method for diffnet_degSeq
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 of the adjacency matrix will considered (faster).
self	Logical scalar. When TRUE autolinks (loops, self edges) are allowed (see details).
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 \(n\times T\). In the case of plot, returns an object of class histogram.

See also

Other statistics: bass, 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

# Comparing degree measurements ---------------------------------------------
# Creating an undirected graph
graph <- rgraph_ba()
graph
#> 11 x 11 sparse Matrix of class "dgCMatrix"
#>    [[ suppressing 11 column names ‘1’, ‘2’, ‘3’ ... ]]
#>                         
#> 1  1 . . . . . . . . . .
#> 2  1 . . . . . . . . . .
#> 3  1 . . . . . . . . . .
#> 4  . 1 . . . . . . . . .
#> 5  1 . . . . . . . . . .
#> 6  . . . . . 1 . . . . .
#> 7  . . . . . 1 . . . . .
#> 8  . 1 . . . . . . . . .
#> 9  . . . . . . . 1 . . .
#> 10 . . . . 1 . . . . . .
#> 11 . . . . 1 . . . . . .

data.frame(
   In=dgr(graph, "indegree", undirected = FALSE),
   Out=dgr(graph, "outdegree", undirected = FALSE),
   Degree=dgr(graph, "degree", undirected = FALSE)
 )
#>    In Out Degree
#> 1   3   0      3
#> 2   2   1      3
#> 3   0   1      1
#> 4   0   1      1
#> 5   2   1      3
#> 6   1   0      1
#> 7   0   1      1
#> 8   1   1      2
#> 9   0   1      1
#> 10  0   1      1
#> 11  0   1      1

# 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)
#> [1] TRUE

# 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)))