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 class '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()

Author

George G. Vega Yon

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)))