`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:

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:

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
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 = 6.10666666666667
#> average module size = 18.75
#> number of components = 1
#> size of largest component = 150
```

We can plot the resulting `igraph`

as an adjacency matrix:

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:

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
#> 18 18 19 22 19 32 11 11
```

Average degree:

We can also label and plot the cluster membership: