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Hypergraph from bipartite group / event data

Source: R/bipartite_groups.R
bipartite_groups.Rd

Constructs a net_hypergraph from long-format event data in which each row records a player participating in a group (a session, team, project, transaction, or any group context). Each unique group becomes one hyperedge spanning the players that appeared in it. Optional weight column produces a weighted incidence matrix.

Usage

bipartite_groups(data, player, group, weight = NULL)

Arguments

data

Data frame in long format. Must contain player and group columns; optionally a weight column.

player

Character. Name of the column whose values become the hypergraph's nodes (players, participants, actors).

group

Character. Name of the column whose values become the hypergraph's hyperedges (groups, sessions, teams).

weight

Character or NULL. If supplied, the column is summed per (player, group) pair to produce a weighted incidence matrix. Default NULL produces a 0/1 binary incidence matrix.

Value

A net_hypergraph object with the same structure produced by build_hypergraph() (hyperedges, incidence, nodes, n_nodes, n_hyperedges, size_distribution, params). The params list records source = "bipartite_groups" and the original column names.

Details

The bipartite representation preserves the full group structure without projecting to a pairwise network. A group of three players A, B, C produces a single 3-hyperedge containing all three, not three pairwise edges AB, AC, BC. This avoids information loss when group interactions are the primary unit of analysis (Perc et al. 2013).

Unlike build_hypergraph() (which derives hyperedges from a network's clique structure), bipartite_groups() takes group memberships directly. The two functions are complementary:

  • bipartite_groups() - when group membership is observed (sessions, transactions, co-authorships).

  • build_hypergraph() - when only pairwise interactions are observed and triadic structure must be inferred from triangles.

Rows with NA in either the player or group column (or, when supplied, the weight column) are dropped silently.

Note

(experimental) Validated against a hand-computed table() incidence reference only; no independent R package exposes the long-format-to-binary-incidence primitive, because the operation is definitionally table(). The code path is a direct one-to-one restatement of its definition.

References

Perc, M., Gomez-Gardenes, J., Szolnoki, A., Floria, L. M., & Moreno, Y. (2013). Evolutionary dynamics of group interactions on structured populations: a review. Journal of the Royal Society Interface 10(80), 20120997. doi:10.1098/rsif.2012.0997

See also

build_hypergraph() for the clique-based constructor.

Examples

df <- data.frame(
  player = c("Alice", "Bob", "Carol", "Alice", "Bob",
             "Dave", "Carol", "Dave", "Eve"),
  session = c("S1", "S1", "S1", "S2", "S2",
              "S3", "S3", "S3", "S3")
)
hg <- bipartite_groups(df, player = "player", group = "session")
print(hg)
#> Hypergraph: 5 nodes, 3 hyperedges
#> Size distribution:
#>   size_2   : 1
#>   size_3   : 2
summary(hg)
#> Hypergraph summary
#>   Nodes:         5
#>   Hyperedges:    3
#>   Mean size:     2.67
#>   Max size:      3
#>    node degree
#> 1 Alice      2
#> 2   Bob      2
#> 3 Carol      2
#> 4  Dave      1
#> 5   Eve      1