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- src/trainers.py: GraphGrAPETrainer → KAFTTrainer; module docstring + comments.
VanillaGrAPETrainer kept as-is (it is a separate control method, not KAFT).
- experiments/: all 19 runners pick up the new class name; result keys
('Cora_GRAFT' etc) become 'Cora_KAFT'; OUT_DIRs renamed (e.g.
bp_graft_depth_20seeds → bp_kaft_depth_20seeds).
- figures/: data-lookup keys + display labels both 'KAFT'; output filename
graft_depth_sweep.{pdf,png} → kaft_depth_sweep.{pdf,png}.
- File rename: experiments/run_bp_graft_depth.py → run_bp_kaft_depth.py;
figures/graft_depth_sweep.pdf → kaft_depth_sweep.pdf.
- README aligned.
Imports verified: from src.trainers import KAFTTrainer succeeds.
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Internal code identifiers (GraphGrAPETrainer class, graft_* file prefixes,
'GRAFT' JSON dataset keys) are kept for backwards compatibility with
results/ and earlier figure caches; a note at the top of the README points
this out so reviewers don't get confused.
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Topology-factorized Jacobian-aligned feedback for deep GNNs. Includes:
- src/: GraphGrAPETrainer (KAFT) + BP / DFA / DFA-GNN / VanillaGrAPE baselines
+ multi-probe alignment estimator + dataset / sparse-mm utilities.
- experiments/: 19 runners reproducing every figure / table in the paper.
- figures/: 4 generators + the 4 PDFs cited in the report.
- paper/: NeurIPS .tex and consolidated experiments_master notes.
Smoke test: 50-epoch Cora GCN L=4 gives BP 77.3% / KAFT 79.0%.
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