Benchmark token_ot_graph_align scalability on synthetic permuted graphs — benchmark_token_ot_graph_align

Generates paired domains where the target is a permuted/noisy copy of the source and reports runtime and basic alignment accuracy as problem size grows.

Usage

benchmark_token_ot_graph_align_scalability(
  sizes = c(50L, 100L, 200L),
  d = 4L,
  noise_sd = 0.01,
  candidate_k = 80L,
  n_levels = 1L,
  prior_mode = c("none", "hard", "soft"),
  token_mode = c("view_only", "view_plus_neighbors"),
  views = "raw",
  n_reps = 3L,
  seed = 1L,
  verbose = FALSE
)

Arguments

sizes: Integer vector of node counts to benchmark.
d: Integer feature dimension for synthetic node features.
noise_sd: Numeric noise standard deviation added to target features.
candidate_k: Integer number of candidates per source node.
n_levels: Integer number of multilevel hierarchy levels.
prior_mode: Prior lifting mode for multilevel (`"none"`, `"hard"`, `"soft"`).
token_mode: Tokenization mode passed to [token_ot_graph_align_control()].
views: Views passed to [token_ot_graph_align_control()].
n_reps: Integer number of replications per size.
seed: Integer seed for reproducibility.
verbose: Logical; print per-run progress.

Value

A data.frame with per-run runtime and summary metrics.

Details

Benchmarks are intended for interactive profiling rather than automated CI.