Many node-classification GNNs apply a linear classifier to a nonnegative mixture of local messages.
An error can reflect either poor mixture weights or a reachable logit set poorly positioned for the classifier.
We separate these causes with an exact-mass linear program and two learned post-hoc repairs.
Every reweighted prediction has an equivalent centered logit translation, but only translations in a message-induced displacement set are realizable by reweighting.
Results
Across eight datasets, eight GNN backbones, and ten splits, mean accuracy rises from 62.6% for the frozen models to 63.8% with reweighting and 65.3% with set-conditioned translation.
A parameter-matched node-only translator reaches 64.6%, showing that translation explains most of the gain while the message set supplies a smaller additional benefit.
Discussion
Although oracle reweighting can correct many errors, label-free reweighting captures little of this potential:
local evidence is often present but hard to select, and relaxing the evidence constraint is more effective than learning within it.