EcoNetGen
lets you randomly generate a wide range of interaction
networks with specified size, average degree, modularity, and
topological structure. You can also sample nodes and links from within
simulated networks randomly, by degree, by module, or by abundance.
Simulations and sampling routines are implemented in ‘FORTRAN’,
providing efficient generation times even for large networks. Basic
visualization methods also included. Algorithms implemented here are
described in de Aguiar et al. (2017)
arXiv:1708.01242.
EcoNetGen
is now on CRAN and can be installed in the usual way:
install.packages("EcoNetGen")
See NEWS for a list of the most recent changes to the
development version and current CRAN release. You can install the
current development version of EcoNetGen
from GitHub with:
# install.packages("devtools")
devtools::install_github("cboettig/EcoNetGen")
This way requires you have a recent FORTRAN compiler available on your machine.
This is a basic example which generates a network. See ?netgen
for
documentation describing the parameter arguments. Setting verbose = FALSE
(default) surpresses the output summary message.
library(EcoNetGen)
set.seed(123456) # for a reproducible simulation
network <- netgen(net_size = 150,
ave_module_size = 20,
min_module_size = 10,
min_submod_size = 5,
net_type = "bi-partite nested",
ave_degree = 10,
verbose = TRUE
)
#>
#> module count = 8
#> average degree = 6.08
#> average module size = 18.75
#> number of components = 1
#> size of largest component = 150
We can plot the resulting igraph
as an adjacency matrix:
adj_plot(network)
Network igraph
objects can also be plotted using the standard igraph
plotting routines, for example:
library(igraph)
plot(network, vertex.size= 0, vertex.label=NA,
edge.color = rgb(.22,0,1,.02), vertex.shape="none",
edge.curved =TRUE, layout = layout_with_kk)
set.seed(123456) # for a reproducible random sampling
sampled <- netsampler(network,
key_nodes_sampler = "degree",
neighbors_sampler = "random",
n_key_nodes = 50,
n_neighbors = 0.5 # 50%
)
We can plot the adjacency network, coloring red the sampled nodes. Note
that adj_plot
objects are just ggplot
graphs (geom_raster
) under
the hood, and can be modified with the usual ggplot
arguments, such as
adding a title and changing the color theme here.
library(ggplot2) # needed to modify plot
adj_plot(sampled) +
ggtitle("Adjacency matrix of sampled vs full network") +
scale_fill_manual(values = c("#ED4E33", "#3B7EA1"))
Don’t forget to check out the ggraph
package, which isn’t required for
EcoNetGen
but provides a lot of additional great ways to plot your
network. Here we plot the simulated network color-coding the sampled
nodes and edges (indicated by the label “sampled” on vertices and
edges):
library(ggraph)
ggraph(sampled, layout = 'kk') +
geom_edge_link(aes(color = label), alpha=0.4) +
geom_node_point(aes(color = label)) +
theme_graph() +
scale_color_manual(values = c("#ED4E33", "#3B7EA1")) +
scale_edge_color_manual(values = c("#ED4E33", "#3B7EA1"))
Or extract and plot just the sampled network:
subnet <- subgraph.edges(sampled,
E(sampled)[label=="sampled"])
ggraph(subnet, layout = 'graphopt') +
geom_edge_link(alpha=0.4) +
geom_node_point() +
theme_graph()
And we can compute common statistics from igraph as well. Here we confirm that clustering by “edge betweeness” gives us the expected number of modules:
community <- cluster_edge_betweenness(as.undirected(network))
length(groups(community))
#> [1] 9
We can check the size of each module as well:
module_sizes <- sizes(community)
module_sizes
#> Community sizes
#> 1 2 3 4 5 6 7 8 9
#> 18 19 31 19 22 7 3 20 11
Average degree:
mean(degree(as.undirected(network)))
#> [1] 6.08
We can also label and plot the cluster membership:
V(sampled)$module <- as.character(membership(community))
ggraph(sampled, layout = 'kk') +
geom_edge_link(alpha=0.1) +
geom_node_point(aes(colour = module)) +
theme_graph()