Pricing Symbolic Rules by What GNNs Leave Unlearned
Symbolic knowledge (motif counts, ring counts, label constraints) is routinely added to graph neural networks (GNNs) and justified by their limited expressivity. We replace this yardstick with an exact one. The largest log-loss reduction a rule $S$ can give a model family is $I(Y;S\midσ)$ given what the family computes; every realised gain splits exactly into this term plus repaid neural debt;…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
