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Create multilayer networks from multiple netify objects

Source: R/layer_netify.R
layer_netify.Rd

layer_netify combines multiple netify objects into a single multilayer network structure. Each input network becomes a layer in the resulting multilayer network, enabling analysis of multiple relationship types or network views simultaneously.

Usage

layer_netify(netlet_list, layer_labels = NULL)

Arguments

netlet_list

A list of netify objects to layer together. All objects must have compatible dimensions and attributes (see Details).

layer_labels

Character vector specifying names for each layer. If NULL (default), uses names from netlet_list or generates generic labels ("layer1", "layer2", etc.). Length must match the number of netify objects.

Value

A multilayer netify object with structure depending on input type:

  • Cross-sectional input: 3D array [actors × actors × layers]

  • Longitudinal array input: 4D array [actors × actors × layers × time]

  • Longitudinal list input: List of 3D arrays, one per time period

The returned object maintains the netify class and includes:

  • Combined nodal attributes (if compatible)

  • Combined dyadic attributes (if compatible)

  • Layer information accessible via attr(obj, 'layers')

  • All standard netify attributes

Details

Compatibility requirements:

All netify objects in netlet_list must have identical:

  • Network type (cross-sectional or longitudinal)

  • Dimensions (same actors and time periods)

  • Symmetry (all directed or all undirected)

  • Mode (all unipartite or all bipartite)

  • Actor composition (same actors in same order)

Attribute handling:

Nodal and dyadic attributes are combined when possible:

  • If attributes are identical across layers, the first layer's attributes are used

  • If attributes differ but have compatible structure, they are merged

  • If attributes are incompatible, a warning is issued and attributes must be added manually using add_node_vars() or add_dyad_vars()

Note

Memory usage increases with the number of layers. For large networks with many layers, consider whether all layers are necessary for your analysis.

Author

Cassy Dorff, Shahryar Minhas

Examples

# Load example data
data(icews)

# Example 1: Cross-sectional multilayer network
icews_10 <- icews[icews$year == 2010,]

# Create separate networks for different interaction types
verbal_coop <- netify(
  icews_10, 
  actor1 = 'i', actor2 = 'j',
  symmetric = FALSE, 
  weight = 'verbCoop',
  nodal_vars = c('i_log_gdp', 'i_log_pop'),
  dyad_vars = 'verbConf'
)

material_coop <- netify(
  icews_10, 
  actor1 = 'i', actor2 = 'j',
  symmetric = FALSE, 
  weight = 'matlCoop',
  nodal_vars = 'i_polity2',
  dyad_vars = 'matlConf'
)

# Layer them together
coop_multilayer <- layer_netify(
  netlet_list = list(verbal_coop, material_coop),
  layer_labels = c('Verbal', 'Material')
)

# Check structure
dim(get_raw(coop_multilayer))  # [actors × actors × 2]
#> [1] 152 152   2
attr(coop_multilayer, 'layers')  # "Verbal" "Material"
#> [1] "Verbal"   "Material"

# Example 2: Longitudinal multilayer (array format)
verbal_longit <- netify(
  icews, 
  actor1 = 'i', actor2 = 'j', time = 'year',
  symmetric = FALSE, 
  weight = 'verbCoop',
  output_format = 'longit_array'
)

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

# Create longitudinal multilayer
longit_multilayer <- layer_netify(
  list(verbal_longit, material_longit),
  layer_labels = c('Verbal', 'Material')
)

dim(get_raw(longit_multilayer))  # [actors × actors × 2 × years]
#> [1] 152 152   2  13