Simulate directed two-mode hyperedge events — simulate_directed_hyperedge

Generates a sequence of directed hyperevents from a sender set $I_m \subseteq V^I$ to a receiver set $J_m \subseteq V^J$, with both $I_m$ and $J_m$ non-empty. This is the directed two-mode counterpart to simulate_hyperedge_events() and matches the data model used in Boschi, Lerner & Wit (2025) Section 5 for citation networks (authors citing papers).

Usage

simulate_directed_hyperedge_events(
  n_events,
  senders,
  receivers,
  min_size_I = 1L,
  max_size_I = 1L,
  min_size_J = 1L,
  max_size_J = 1L,
  baseline_rate = 1,
  endogenous_stats = character(0),
  endogenous_effects = numeric(0),
  start_time = 0
)

Arguments

n_events: Number of events to simulate.
senders: Character vector of sender names $V^I$.
receivers: Character vector of receiver names $V^J$. Must be non-empty.
min_size_I, max_size_I: Sender-side cardinality bounds.
min_size_J, max_size_J: Receiver-side cardinality bounds.
baseline_rate: Multiplicative baseline ($\lambda_0$).
endogenous_stats: Character vector of supported stat names: "size_I" (sender-side size penalty), "size_J" (receiver-side), "activity" (number of past events covering the full focal (I, J)), "subrep_<rho>_<l>" (directed subset repetition, paper eq. 4).
endogenous_effects: Numeric vector of coefficients, same length and order as endogenous_stats.
start_time: Simulation start time.

Value

A directed hyperedge log (amorem_hyperedge_log data frame with I, J, time columns; J non-empty on every row).

Details

At each step the simulator enumerates every candidate hyperedge $(I, J)$ with $|I| \in [\mathrm{min\_size\_I}, \mathrm{max\_size\_I}]$ and $|J| \in [\mathrm{min\_size\_J}, \mathrm{max\_size\_J}]$, computes the rate $$ \lambda(t, I, J) \;=\; \mathrm{baseline\_rate} \;\cdot\; \exp\!\left(\sum_k \beta_k \, x_k(t, I, J)\right), $$ and draws one event proportional to its rate. The waiting time is exponential with rate equal to the total intensity.

Candidate-space size is exponential in $|V^I|$ and $|V^J|$, so practical use is limited to small actor / item universes.

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, Section 5.

Examples

# \donttest{
hl <- simulate_directed_hyperedge_events(
  n_events  = 40,
  senders   = paste0("a", 1:4),
  receivers = paste0("p", 1:4),
  max_size_I = 2, max_size_J = 2,
  baseline_rate = 0.3,
  endogenous_stats   = c("subrep_1_1", "size_I"),
  endogenous_effects = c(subrep_1_1 = 0.8, size_I = -0.4))
# }