Joining parts of a network

These functions join a list of networks that are each a part of one network, holding some of its nodes and the ties among them, back into that one network. Each reverses one of the to_*s() functions that splits a network into parts.

• from_subgraphs() joins a list of subgraphs, as to_subgraphs() returns, into one network.

• from_egos() joins a list of ego networks, as to_egos() returns, into one whole network.

A node or a tie that more than one part holds is held once in the result.

Every function here returns the class of the networks in the list: a 'stocnet' from 'stocnet's, a 'tbl_graph' from 'tbl_graph's, an 'igraph' from 'igraph's, a 'network' from 'network's, and a single matrix from matrices, since the parts are all of one network.

To stack networks that each hold the same nodes, one for each moment, layer, or reporter, see from_times() and the other functions there.

from_subgraphs(netlist)

from_egos(netlist)

Arguments

netlist

A list of network, igraph, tidygraph, matrix, or edgelist objects.

Value

An object of the same class as the function was given, modified as explained in the function description, details, or section. Functions that split a network return a list of such objects.

See also

Other modifications: modif_backbone, modif_concepts, modif_direction, modif_from_ternary, modif_labels, modif_levels, modif_miss, modif_motifs, modif_paths, modif_permutation, modif_plexity, modif_project, modif_proximity, modif_scope, modif_split, modif_weight

Examples

ison_adolescents |>
  mutate(unicorn = sample(c("yes", "no"), 8, replace = TRUE)) |>
  to_subgraphs(attribute = "unicorn") |>
  from_subgraphs()
#> # A labelled, undirected network of 8 nodes and 3 ties
#> 
#> ── Nodes 
#> # A tibble: 8 × 3
#>   name  unicorn.x unicorn.y
#>   <chr> <chr>     <chr>    
#> 1 Betty no        NA       
#> 2 Dale  no        NA       
#> 3 Pam   no        NA       
#> 4 Sue   NA        yes      
#> 5 Alice NA        yes      
#> 6 Jane  NA        yes      
#> # ℹ 2 more rows
#> 
#> ── Ties 
#> # A tibble: 3 × 2
#>    from    to
#>   <int> <int>
#> 1     4     5
#> 2     5     6
#> 3     7     8
#> 
ison_adolescents |>
  to_egos() |>
  from_egos()
#> # A labelled, directed network of 8 nodes and 10 arcs
#> 
#> ── Nodes 
#> # A tibble: 8 × 1
#>   name 
#>   <chr>
#> 1 Betty
#> 2 Sue  
#> 3 Alice
#> 4 Jane 
#> 5 Pam  
#> 6 Carol
#> # ℹ 2 more rows
#> 
#> ── Ties 
#> # A tibble: 10 × 2
#>    from    to
#>   <int> <int>
#> 1     1     2
#> 2     2     3
#> 3     2     7
#> 4     3     7
#> 5     2     5
#> 6     3     5
#> # ℹ 4 more rows
#>