`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")
::install_github("cboettig/EcoNetGen") devtools
```

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) suppresses the
output summary message.

```
library(EcoNetGen)
set.seed(123456) # for a reproducible simulation
<- netgen(net_size = 150,
network 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 = 6.10666666666667
#> 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
<- netsampler(network,
sampled 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:

```
<- subgraph.edges(sampled,
subnet 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:

```
<- cluster_edge_betweenness(as.undirected(network))
community length(groups(community))
#> [1] 8
```

We can check the size of each module as well:

```
<- sizes(community)
module_sizes
module_sizes#> Community sizes
#> 1 2 3 4 5 6 7 8
#> 18 18 19 22 19 32 11 11
```

Average degree:

```
mean(degree(as.undirected(network)))
#> [1] 6.12
```

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()
```