# Attribution confidence at the scale the report quotes (section 3.4).
#
# Capturing a cascade identifies the MESSAGE, not the originator: an honest node seen transmitting
# may simply have been passing one along. This run measures both ends of the resulting bracket.
#
#   local        -- confidence d/(2d-a) from the sender's own a adversarial peers, and the share of
#                   honest nodes attributable at 0.5 / 0.9 / 0.99.
#   neighbourhood -- 1/(1+(1-f)^L), where the adversary also sees the message anywhere upstream.
#                    L is fixed by the graph, so upstream_hops is recorded per run.
#
# Propagation is not the subject, so n_rounds is minimal: the adversary and deanonymization metrics
# are closed-form and exact, and the hop distance is a property of the topology.
n_nodes: [20000]
degree: [4, 8, 16]
blend_hops: [3]
max_blend_delay: [3]
unresponsive_frac: [0.0]
f_adv: [0.1, 0.2, 0.33, 0.5]
adversary_mode: [random, worstcase_coverage]
seeds: 4
base:
  n_rounds: 10
  n_placements: 4