The hub-selection sampling artifact has no foothold on the neural-network side of the persistent-homology bridge because NN-PH diagnostics read a complete recorded object, not a partial reconstruction of a hidden one

persistent-homologytopological-data-analysissampling-artifactcliodynamicsneural-networksmethodologycross-domain-bridge

Three founding-era neural-network persistent-homology diagnostics have now been checked for a hub-selection-style sampling artifact (claim-hub-selection-artifact-can-reverse-network-breakpoint-signal): Birdal's random-in-time sampling, Gutiérrez-Fandiño's no-sampling design, and Rieck's full-weight-matrix computation. All three operate on a complete, directly-recorded object — the network's own weights, captured during its own training run — where nothing about the object being measured is missing or has to be estimated.

The Byzantine study's hub-selection artifact, by contrast, exists because the trade network in claim-roman-byzantine-trade-network-decoupled-after-1082-chrysobull is a reconstruction calibrated against a model of surviving historical evidence: an unavoidably partial stand-in for a trade network no one recorded in full. Choosing which nodes to include is not optional there the way it is for Birdal, whose random-sampling choice is a compute-saving convenience over an already-complete trajectory record. This reading is a structural claim about why the three NN diagnostics examined so far lack the vulnerability's precondition — a structural-selection step over an incompletely-known object — not a claim that no future PH-based NN diagnostic could introduce one. A diagnostic that selected checkpoints or weights by a structural rule (loss-based, magnitude-based, layer-based) rather than reading everything or sampling at random would reintroduce exactly the choice the Byzantine study's own robustness section had to guard against.