These functions offer tools for projecting manynet-consistent data:
to_mode1() projects a two-mode network to a one-mode network
of the first node set's (e.g. rows) joint affiliations to nodes in the second node set (columns).
to_mode2() projects a two-mode network to a one-mode network
of the second node set's (e.g. columns) joint affiliations to nodes in the first node set (rows).
to_ties() projects a network to one where the ties become nodes and incident nodes become their ties.
to_galois() projects a network to its Galois derivation.
An object of a manynet-consistent class:
matrix (adjacency or incidence) from {base} R
edgelist, a data frame from {base} R or tibble from {tibble}
igraph, from the {igraph} package
network, from the {network} package
tbl_graph, from the {tidygraph} package
Method for establishing ties, currently "count" (default), "jaccard", or "rand". "count" calculates the number of coinciding ties, and can be interpreted as indicating the degree of opportunities between nodes. "jaccard" uses this count as the numerator in a proportion, where the denominator consists of any cell where either node has a tie. It can be interpreted as opportunity weighted by participation. "rand", or the Simple Matching Coefficient, is a proportion where the numerator consists of the count of cells where both nodes are present or both are absent, over all possible cells. It can be interpreted as the (weighted) degree of behavioral mirroring between two nodes. "pearson" (Pearson's coefficient) and "yule" (Yule's Q) produce correlations for valued and binary data, respectively. Note that Yule's Q has a straightforward interpretation related to the odds ratio.
All to_ functions return an object of the same class as that provided.
So passing it an igraph object will return an igraph object
and passing it a network object will return a network object,
with certain modifications as outlined for each function.
Not all functions have methods available for all object classes. Below are the currently implemented S3 methods:
| data.frame | igraph | matrix | network | tbl_graph | |
| to_mode1 | 1 | 1 | 1 | 1 | 1 |
| to_mode2 | 1 | 1 | 1 | 1 | 1 |
| to_ties | 1 | 1 | 1 | 1 | 1 |
Note that the output from to_galois() is very busy at the moment.
to_mode1(ison_southern_women)
#> # A labelled, weighted, undirected network with 18 nodes and 139 ties
#> # A tibble: 18 × 1
#> name
#> <chr>
#> 1 Evelyn
#> 2 Laura
#> 3 Theresa
#> 4 Brenda
#> 5 Charlotte
#> 6 Frances
#> # ℹ 12 more rows
#> # A tibble: 139 × 3
#> from to weight
#> <int> <int> <dbl>
#> 1 1 2 6
#> 2 1 4 6
#> 3 1 3 7
#> 4 1 5 3
#> 5 1 6 4
#> 6 1 7 3
#> # ℹ 133 more rows
to_mode2(ison_southern_women)
#> # A labelled, weighted, undirected network with 14 nodes and 66 ties
#> # A tibble: 14 × 1
#> name
#> <chr>
#> 1 E1
#> 2 E2
#> 3 E3
#> 4 E4
#> 5 E5
#> 6 E6
#> # ℹ 8 more rows
#> # A tibble: 66 × 3
#> from to weight
#> <int> <int> <dbl>
#> 1 1 2 2
#> 2 1 3 3
#> 3 1 4 2
#> 4 1 5 3
#> 5 1 6 3
#> 6 1 8 3
#> # ℹ 60 more rows
#autographr(to_mode1(ison_southern_women))
#autographr(to_mode2(ison_southern_women))
to_ties(ison_adolescents)
#> # A labelled, undirected network with 10 nodes and 20 ties
#> # A tibble: 10 × 1
#> name
#> <chr>
#> 1 Betty-Sue
#> 2 Sue-Alice
#> 3 Alice-Jane
#> 4 Sue-Dale
#> 5 Alice-Dale
#> 6 Jane-Dale
#> # ℹ 4 more rows
#> # A tibble: 20 × 2
#> from to
#> <int> <int>
#> 1 1 2
#> 2 2 3
#> 3 1 4
#> 4 2 4
#> 5 4 5
#> 6 2 5
#> # ℹ 14 more rows
#autographr(to_ties(ison_adolescents))