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Convert netify objects to dbn format

Source: R/to_dbn.R
netify_to_dbn.Rd

netify_to_dbn (also available as to_dbn) transforms netify network objects into the array format required by the dbn package for Dynamic Bilinear Network models. This enables the use of longitudinal latent space models for multilayer and single-layer networks.

Usage

netify_to_dbn(netlet)

to_dbn(netlet)

Arguments

netlet

A netify object (class "netify") containing longitudinal network data. Must be of type longit_array or longit_list. Supports both single-layer and multilayer networks.

Value

A list containing:

Y

Network adjacency data as a 4D array of dimensions [n_actors, n_actors, n_layers, n_time]. For single-layer networks, n_layers = 1 and the third dimension is labeled with the weight variable name (or "edge_value" for binary networks). Missing edges are preserved as NA.

Xdyad

Dyadic covariates as a 4D array of dimensions [n_actors, n_actors, n_covariates, n_time], or NULL if none exist.

Xrow

Sender/row actor attributes as a 3D array of dimensions [n_actors, n_attributes, n_time], or NULL if none exist.

Xcol

Receiver/column actor attributes as a 3D array of dimensions [n_actors, n_attributes, n_time], or NULL if none exist. For symmetric networks, Xcol is identical to Xrow.

Details

The dbn package expects a 4D array with dimensions [n, n, p, T] where n is the number of actors, p is the number of relation types (layers), and T is the number of time periods.

Supported netify types:

  • longit_array: Directly extracts or reshapes the underlying array

  • longit_list: Converts to array format first (actors are unioned across time, missing entries become NA)

Cross-sectional networks are not supported since dbn is designed for longitudinal data.

Variable requirements:

  • All nodal attributes must be numeric (integer or double)

  • Character or factor variables must be converted before using this function

Author

Shahryar Minhas

Examples

# Load example data
data(icews)

# Create two longitudinal networks
verbal_net <- netify(
    icews,
    actor1 = "i", actor2 = "j", time = "year",
    symmetric = FALSE,
    weight = "verbCoop",
    output_format = "longit_array"
)

material_net <- netify(
    icews,
    actor1 = "i", actor2 = "j", time = "year",
    symmetric = FALSE,
    weight = "matlCoop",
    output_format = "longit_array"
)

# Create multilayer network
multi_net <- layer_netify(
    list(verbal_net, material_net),
    layer_labels = c("Verbal", "Material")
)

# Convert to dbn format
dbn_data <- netify_to_dbn(multi_net)
dim(dbn_data$Y) # [n_actors, n_actors, 2, n_years]
#> [1] 152 152   2  13

if (FALSE) { # \dontrun{
# Single-layer also works
dbn_single <- netify_to_dbn(verbal_net)
dim(dbn_single$Y) # [n_actors, n_actors, 1, n_years]
} # }