From bd9333eda60a9029a198acaeacb1eca4312bd1e8 Mon Sep 17 00:00:00 2001
From: YurenHao0426 <blackhao0426@gmail.com>
Date: Mon, 4 May 2026 23:05:16 -0500
Subject: =?UTF-8?q?Initial=20release:=20GRAFT=20(KAFT)=20=E2=80=94=20NeurI?=
 =?UTF-8?q?PS=202026=20submission=20code?=
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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%.
---
 experiments/run_depth_extras.py | 92 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 experiments/run_depth_extras.py

(limited to 'experiments/run_depth_extras.py')

diff --git a/experiments/run_depth_extras.py b/experiments/run_depth_extras.py
new file mode 100644
index 0000000..66a7d45
--- /dev/null
+++ b/experiments/run_depth_extras.py
@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""H11: Fill depth sweep at L=14 and L=18 to densify Fig 4(a).
+3 methods (BP / DFA-GNN / GRAFT) × 4 datasets × 2 depths × 20 seeds = 480 runs.
+"""
+
+import torch
+import numpy as np
+import json
+import os
+from src.data import load_dataset
+from src.trainers import BPTrainer, DFAGNNTrainer, GraphGrAPETrainer
+from run_dblp_depth import load_dblp
+
+device = 'cuda:0'
+SEEDS = list(range(20))
+EPOCHS = 200
+DEPTHS = [14, 18]
+OUT_DIR = 'results/depth_extras_20seeds'
+
+grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
+                   lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')
+dfagnn_extra = dict(diffusion_alpha=0.5, diffusion_iters=10, max_topo_power=3)
+
+METHODS = {
+    'BP':      (BPTrainer, {}),
+    'DFA-GNN': (DFAGNNTrainer, dfagnn_extra),
+    'GRAFT':   (GraphGrAPETrainer, grape_extra),
+}
+
+
+def train_one(cls, common, extra, seed):
+    torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
+    t = cls(**common, **extra)
+    if hasattr(t, 'align_mode'):
+        t.align_mode = 'chain_norm'
+    bv, bt = 0, 0
+    for ep in range(EPOCHS):
+        t.train_step()
+        if ep % 5 == 0:
+            v = t.evaluate('val_mask')
+            te = t.evaluate('test_mask')
+            if v > bv: bv, bt = v, te
+    del t; torch.cuda.empty_cache()
+    return bt
+
+
+def main():
+    os.makedirs(OUT_DIR, exist_ok=True)
+    per_seed_file = os.path.join(OUT_DIR, 'per_seed_data.json')
+    if os.path.exists(per_seed_file):
+        with open(per_seed_file) as f:
+            per_seed_data = json.load(f)
+    else:
+        per_seed_data = {}
+
+    datasets_cfg = {
+        'Cora': lambda: load_dataset('Cora', device=device),
+        'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
+        'PubMed': lambda: load_dataset('PubMed', device=device),
+        'DBLP': lambda: load_dblp(),
+    }
+
+    for ds_name, loader in datasets_cfg.items():
+        data = loader()
+        for L in DEPTHS:
+            common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
+                          num_layers=L, residual_alpha=0.0, backbone='gcn')
+            for mname, (cls, extra) in METHODS.items():
+                key = f"{ds_name}_L{L}_{mname}"
+                if key not in per_seed_data:
+                    per_seed_data[key] = {}
+                print(f"\n=== {key} (20 seeds) ===", flush=True)
+                for seed in SEEDS:
+                    sk = str(seed)
+                    if sk in per_seed_data[key]:
+                        continue
+                    try:
+                        acc = train_one(cls, common, extra, seed)
+                        per_seed_data[key][sk] = acc
+                        print(f"  seed {seed}: {acc*100:.1f}%", flush=True)
+                    except Exception as e:
+                        print(f"  seed {seed}: FAILED - {e}", flush=True)
+                        per_seed_data[key][sk] = 0.0
+                    with open(per_seed_file, 'w') as f:
+                        json.dump(per_seed_data, f, indent=2)
+        del data; torch.cuda.empty_cache()
+
+    print(f"\nDone. Saved to {per_seed_file}")
+
+
+if __name__ == '__main__':
+    main()
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