network_leiden: Implement the Leiden community detection on a Person to Person network query

Description

Take a P2P network query and implement the Leiden community detection method. To run this function, you will require all the pre-requisites of the 'leiden' package installed, which includes Python and 'reticulate'.

Usage

network_leiden(
  data,
  hrvar = "Organization",
  bg_fill = "#000000",
  font_col = "#FFFFFF",
  algorithm = "mds",
  path = "network_p2p_leiden",
  node_alpha = 0.8,
  res = 0.5,
  seed = 1,
  desc_hrvar = c("Organization", "LevelDesignation", "FunctionType"),
  return = "plot-leiden",
  size_threshold = 5000
)

Value

See return.

Arguments

data

Data frame containing a person-to-person query.

hrvar

String containing the label for the HR attribute.

bg_fill

String to specify background fill colour.

font_col

String to specify font and link colour.

algorithm

String to specify the node placement algorithm to be used. Defaults to "mds" for the deterministic multi-dimensional scaling of nodes. See https://rdrr.io/cran/ggraph/man/layout_tbl_graph_igraph.html for a full list of options.

path

File path for saving the PDF output. Defaults to a timestamped path based on current parameters.

node_alpha

A numeric value between 0 and 1 to specify the transparency of the nodes. Defaults to 0.7.

res

Resolution parameter to be passed to leiden::leiden(). Defaults to 0.5.

seed

Seed for the random number generator passed to either set.seed() when the Louvain algorithm is used, or leiden::leiden() when the Leiden algorithm is used, to ensure consistency. Only applicable when display is set to "louvain" or "leiden".

desc_hrvar

Character vector of length 3 containing the HR attributes to use when returning the "describe" output. See network_describe().

return

String specifying what output to return. Defaults to "plot-leiden". Valid return options include:

'plot-leiden': return a network plot coloured by leiden communities, saving a PDF to path.
'plot-hrvar': return a network plot coloured by HR attribute, saving a PDF to path.
'plot-sankey': return a sankey plot combining communities and HR attribute.
'table': return a vertex summary table with counts in communities and HR attribute.
'data': return a vertex data file that matches vertices with communities and HR attributes.
'describe': return a list of data frames which describe each of the identified communities. The first data frame is a summary table of all the communities.
'network': return 'igraph' object.

size_threshold

Numeric value representing the maximum number of edges before network_leiden() switches to use a more efficient, but less elegant plotting method (native igraph). Defaults to 5000. Set as 0 to coerce to a fast plotting method every time, and Inf to always use the default plotting method (with 'ggraph').

Simulating and running Leiden Community Detection

Below is an example on how to simulate a network and run the function.

# Simulate a small person-to-person dataset
p2p_data <- p2p_data_sim(size = 50)
# Return leiden, console, plot
p2p_data %>%
  network_leiden(path = NULL,
                 return = "plot")
```

Description

Usage

Value

Arguments

Simulating and running Leiden Community Detection

See Also