Network summary plots — netsummary

Draw a network summary plot proposed by Maugis et al. (2017). To count k-cycles, Alon et al. (1997) is used.

Usage

netsummary_plot(
  A,
  subsample_sizes = NA,
  max_cycle_order = 4,
  n_rep = NA,
  n_subsample_sizes = 11,
  alpha = 0.05,
  y_max = NA,
  save_plot = FALSE,
  filename = "myplot.pdf",
  width = 7,
  height = 5,
  max_subsample_size = 250,
  ...
)

violin_netsummary(A, ...)

Arguments

A: an adjacency matrix, igraph object, or network object to draw a network summary plot. It must be an undirected and simple graph.
subsample_sizes: a numeric vector of vertex subsample sizes. If NA, the subsample size is selected automatically.
max_cycle_order: an integer value of the maximum cycle size. Must be >=3 and <=7.
n_rep: an integer value of subsampling replication. If NA, n_rep is automatically selected by alpha.
n_subsample_sizes: number of different subsample sizes for automatic selection. It is only used when subsample_sizes = NA.
alpha: a pre-specified level used in determining n_rep and subsample_sizes when they are not specified. It must be in (0,1). Default is 0.05. Smaller alpha gives larger n_rep and subsample_sizes.
y_max: Upper limit of y-axis of the plot. Must be 0 < y_max <= 1. If NA, the upper limit is automatically selected.
save_plot: A logical indicating whether to save the generated figure. If TRUE, the plot is saved via ggplot2::ggsave() using the specified file name. Otherwise, the plot is displayed.
filename: file name to save the generated figure.
width: a numeric value of the width of the generated figure in inch. It is only used when save_plot = TRUE.
height: a numeric value of the height of the generated figure in inch. It is only used when save_plot = TRUE.
max_subsample_size: integer. Upper bound on the automatically selected subsample size. Larger values improve statistical accuracy but increase computation time. Default is 250.
...: Pass R, Ns, y.max, or save.plot via the renamed arguments n_rep, n_subsample_sizes, y_max, and save_plot instead.

Value

A ggplot object. Printed as a side effect when save_plot = FALSE. Returns the plot invisibly when save_plot = TRUE.

Details

Vertex sampling is done by simple random sampling without replacement.

The automatically selected subsample size is capped at max_subsample_size to limit computation time.

Each violin shows the distribution of the subsampled statistic, and a dot marks the mean.

The following input classes are supported: base::matrix, Matrix::dgCMatrix, igraph::igraph, network::network.

References

Maugis et al. (2017). Topology reveals universal features for network comparison. arXiv: 1705.05677

Alon et al. (1997). Finding and counting given length cycles. Algorithmica 17, 209–223 (1997). https://doi.org/10.1007/BF02523189

Examples

{
set.seed(2022)
#Generating Erdos-Renyi graph
n <- 400
#igraph object
A <- igraph::sample_gnp(n, 0.05)
netsummary_plot(A)
}
#> Use n_rep = 574

# \donttest{
#sparse adjacency matrix
A2 <- igraph::as_adjacency_matrix(A)
netsummary_plot(A2)
#> Use n_rep = 574


#dense adjacency matrix
A2 <- igraph::as_adjacency_matrix(A, sparse = FALSE)
netsummary_plot(A2)
#> Use n_rep = 574


#user-specified n_rep and subsample_sizes
netsummary_plot(A, n_rep = 500, subsample_sizes = 150)


#user-specified alpha
netsummary_plot(A, alpha = 0.1)
#> Use n_rep = 114


#network object
A3 <- network::as.network(igraph::as_adjacency_matrix(A, sparse = FALSE))
netsummary_plot(A3)
#> Use n_rep = 574


#user-specified max_subsample_size
netsummary_plot(A, max_subsample_size = 100)
#> Use n_rep = 574


#saving the plot with user-specified file name
netsummary_plot(A, save_plot = TRUE,
                filename = file.path(tempdir(), "myfig.pdf"))
#> Use n_rep = 574
# }