Simulate undirected hyperedge events (multi-actor meetings) — simulate_hyperedge

Generates a sequence of undirected hyperevents — meetings of varying size drawn from the actor set actors — under a linear hyperedge model. Mirrors the simulation setup of Boschi, Lerner & Wit (2025) Section 4: each event is a subset of actors with size in 1..max_size, fired with rate $$ \lambda(t, I) \;=\; \mathrm{baseline\_rate} \;\cdot\; \exp\!\left(\sum_k \beta_k \, x_k(t, I)\right), $$ where each $x_k(t, I)$ is one of the hyperedge-native covariates supported by hyperedge_features() (activity, subrep_<rho> for undirected events) or size (the event's cardinality $|I|$).

Usage

simulate_hyperedge_events(
  n_events,
  actors,
  max_size,
  baseline_rate,
  endogenous_stats = character(0),
  endogenous_effects = numeric(0),
  start_time = 0,
  min_size = 1L
)

Arguments

n_events: Number of events to simulate.
actors: Character vector of actor names.
max_size: Maximum allowed meeting size ($w$ in the paper). Must be in 1..length(actors).
baseline_rate: Multiplicative baseline ($\lambda_0$).
endogenous_stats: Character vector of stat names accepted by hyperedge_features() (undirected variants — activity, subrep_1, subrep_2, ...) or the literal "size" (the event's cardinality).
endogenous_effects: Numeric vector of coefficients, same length and order as endogenous_stats.
start_time: Simulation start time.
min_size: Minimum allowed meeting size. Defaults to 1.

Value

A hyperedge log (see hyperedge_log()) with n_events rows.

Details

At each step the simulator enumerates every subset of actors with size in 1..max_size. The per-event work is therefore $O\!\left(\sum_{s=1}^{w} \binom{|V|}{s}\right)$; practical for small actor counts (e.g. $|V| \le 20$, max_size <= 4).

References

Boschi M, Lerner J, Wit EC (2025). Beyond Linearity and Time-Homogeneity: Relational Hyper Event Models with Time-Varying Non-Linear Effects. arXiv:2509.05289.

Examples

# \donttest{
# Five-actor meetings of size up to 3, with weak attractor on
# repeated triads and a size penalty:
hl <- simulate_hyperedge_events(
  n_events = 50,
  actors   = LETTERS[1:5],
  max_size = 3,
  baseline_rate = 0.2,
  endogenous_stats   = c("subrep_2", "size"),
  endogenous_effects = c(subrep_2 = 0.5, size = -0.3))
# }