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Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 8 |
1 files changed, 4 insertions, 4 deletions
@@ -1,7 +1,7 @@ # KAFT: Topology-Factorized Jacobian-Aligned Feedback for Deep GNNs -> Internal class names (`GraphGrAPETrainer`, file paths starting with -> `graft_*` / dataset keys `'GRAFT'`) are the original code identifier. +> Internal class names (`KAFTTrainer`, file paths starting with +> `kaft_*` / dataset keys `'KAFT'`) are the original code identifier. > The method is referred to as **KAFT** in the paper. Code release accompanying the NeurIPS 2026 submission. @@ -24,7 +24,7 @@ computed in O(1) parallel depth on GPUs. ``` src/ core method - trainers.py BPTrainer, GraphGrAPETrainer (= KAFT), DFA/DFA-GNN, alignment + trainers.py BPTrainer, KAFTTrainer (= KAFT), DFA/DFA-GNN, alignment data.py PyG dataset loaders, normalized  / row-Â, sparse-mm helpers experiments/ one runner per reported result block (see `## Reproducing`) figures/ figure generators + the four rendered PDFs in the paper @@ -51,7 +51,7 @@ CUDA_VISIBLE_DEVICES=0 python -u experiments/run_ablation_20seeds.py # Fig 2: Planetoid depth sweep (11 / 13 points) CUDA_VISIBLE_DEVICES=0 python -u experiments/run_shallow_depth.py -CUDA_VISIBLE_DEVICES=0 python -u experiments/run_bp_graft_depth.py +CUDA_VISIBLE_DEVICES=0 python -u experiments/run_bp_kaft_depth.py CUDA_VISIBLE_DEVICES=0 python -u experiments/run_dfagnn_depth.py CUDA_VISIBLE_DEVICES=0 python -u experiments/run_depth_extras.py CUDA_VISIBLE_DEVICES=0 python -u experiments/run_dblp_depth_scaling.py |