Initial release: GRAFT (KAFT) — NeurIPS 2026 submission code

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%.

1 files changed, 93 insertions, 0 deletions

diff --git a/figures/gen_realworld_depth_fig.py b/figures/gen_realworld_depth_fig.py
new file mode 100644
index 0000000..1ff7e2d
--- /dev/null
+++ b/figures/gen_realworld_depth_fig.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python3
+"""Real-world dataset depth-sweep figure (Fig 4(a)' style).
+4 panels: CFull-CiteSeer, CFull-DBLP, CFull-PubMed (biomed), Coauthor-Physics.
+Data hardcoded from cfull_paper_setup.log + dblpfull_full_depth.log +
+pubmedfull_full_depth.log + physics_full_depth.log + dblp_paper_setup.log + cs_paper_setup.log."""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import to_rgba
+
+# Aggregated paper-setup data: (mean, std) for BP and GRAFT at each depth
+DATA = {
+    'CFull-CiteSeer': {
+        'depths': [3, 5, 8, 10, 12, 14, 16, 18, 20],
+        'BP':      [(0.870, 0.0072), (0.860, 0.0056), (0.825, 0.0208), (0.549, 0.1164), (0.365, 0.0209), (0.297, 0.0421), (0.230, 0.0209), (0.238, 0.0131), (0.209, 0.0319)],
+        'DFA':     [(0.855, 0.0044), (0.834, 0.0106), (0.566, 0.0289), (0.425, 0.0993), (0.329, 0.1060), (0.368, 0.0604), (0.297, 0.0722), (0.243, 0.0661), (0.244, 0.0667)],
+        'DFA-GNN': [(0.858, 0.0038), (0.826, 0.0187), (0.581, 0.1085), (0.465, 0.0698), (0.289, 0.0677), (0.296, 0.1372), (0.244, 0.0673), (0.211, 0.0204), (0.193, 0.0051)],
+        'GRAFT':   [(0.857, 0.0006), (0.846, 0.0019), (0.829, 0.0021), (0.780, 0.0197), (0.667, 0.0630), (0.487, 0.0621), (0.430, 0.1145), (0.369, 0.0089), (0.380, 0.0258)],
+    },
+    'CFull-DBLP': {
+        'depths': [3, 5, 8, 10, 12, 14, 16, 18, 20],
+        'BP':      [(0.826, 0.0027), (0.814, 0.0006), (0.793, 0.0070), (0.710, 0.1180), (0.652, 0.0728), (0.559, 0.1132), (0.454, 0.0065), (0.469, 0.0077), (0.461, 0.0144)],
+        'DFA':     [(0.829, 0.0031), (0.819, 0.0076), (0.736, 0.0409), (0.703, 0.0025), (0.682, 0.0257), (0.548, 0.1104), (0.532, 0.1206), (0.533, 0.1209), (0.447, 0.0000)],
+        'DFA-GNN': [(0.832, 0.0024), (0.823, 0.0033), (0.766, 0.0362), (0.617, 0.1203), (0.617, 0.1203), (0.523, 0.1018), (0.447, 0.0000), (0.447, 0.0000), (0.531, 0.1187)],
+        'GRAFT':   [(0.827, 0.0024), (0.825, 0.0090), (0.813, 0.0121), (0.786, 0.0032), (0.730, 0.0315), (0.701, 0.0020), (0.700, 0.0001), (0.610, 0.1150), (0.613, 0.1175)],
+    },
+    'CFull-PubMed (biomed)': {
+        'depths': [3, 5, 8, 10, 12, 14, 16, 18, 20],
+        'BP':      [(0.845, 0.0018), (0.833, 0.0023), (0.825, 0.0026), (0.824, 0.0025), (0.699, 0.0096), (0.499, 0.1413), (0.399, 0.0000), (0.500, 0.1421), (0.399, 0.0000)],
+        'DFA':     [(0.822, 0.0041), (0.793, 0.0188), (0.585, 0.1353), (0.531, 0.0768), (0.484, 0.0833), (0.431, 0.0446), (0.427, 0.0383), (0.399, 0.0000), (0.399, 0.0000)],
+        'DFA-GNN': [(0.822, 0.0040), (0.750, 0.0551), (0.604, 0.1572), (0.522, 0.1154), (0.462, 0.0888), (0.399, 0.0000), (0.438, 0.0550), (0.399, 0.0000), (0.466, 0.0945)],
+        'GRAFT':   [(0.830, 0.0068), (0.814, 0.0049), (0.789, 0.0099), (0.732, 0.0713), (0.690, 0.0585), (0.646, 0.0134), (0.603, 0.0086), (0.545, 0.1031), (0.525, 0.0887)],
+    },
+    'Coauthor-Physics': {
+        'depths': [3, 5, 8, 10, 12, 14, 16, 18, 20],
+        'BP':      [(0.949, 0.0005), (0.943, 0.0014), (0.937, 0.0011), (0.829, 0.0344), (0.818, 0.0387), (0.770, 0.0151), (0.743, 0.0038), (0.682, 0.1000), (0.521, 0.0215)],
+        'DFA':     [(0.948, 0.0007), (0.920, 0.0067), (0.711, 0.0227), (0.686, 0.1275), (0.560, 0.0751), (0.506, 0.0005), (0.557, 0.0737), (0.559, 0.0762), (0.505, 0.0000)],
+        'DFA-GNN': [(0.947, 0.0012), (0.836, 0.0451), (0.712, 0.0369), (0.567, 0.0720), (0.505, 0.0003), (0.505, 0.0000), (0.505, 0.0000), (0.559, 0.0756), (0.505, 0.0000)],
+        'GRAFT':   [(0.947, 0.0008), (0.943, 0.0004), (0.922, 0.0092), (0.867, 0.0368), (0.749, 0.0423), (0.686, 0.0122), (0.614, 0.0771), (0.666, 0.0010), (0.667, 0.0003)],
+    },
+}
+
+COLORS = {'BP': '#888888', 'DFA': '#7A5BAA', 'DFA-GNN': '#3B7AC2', 'GRAFT': '#C23B3B'}
+GRID = '#ECEFF3'
+TEXT = '#2F3437'
+
+plt.rcParams.update({
+    'font.size': 9, 'axes.labelsize': 9,
+    'xtick.labelsize': 8, 'ytick.labelsize': 8, 'legend.fontsize': 9,
+    'pdf.fonttype': 42, 'ps.fonttype': 42,
+})
+
+fig, axes = plt.subplots(1, 4, figsize=(13.0, 3.0))
+
+datasets = list(DATA.keys())
+legend_handles = {}
+for ax, ds in zip(axes, datasets):
+    d = DATA[ds]
+    xs = d['depths']
+    for method in ['BP', 'DFA', 'DFA-GNN', 'GRAFT']:
+        means = np.array([v[0] for v in d[method]])
+        stds = np.array([v[1] for v in d[method]])
+        c = COLORS[method]
+        line, = ax.plot(xs, means, marker='o', markersize=5, color=c, linewidth=1.6,
+                        markerfacecolor=to_rgba(c, alpha=0.35), markeredgecolor=c,
+                        markeredgewidth=0.8, zorder=3)
+        ax.fill_between(xs, means - stds, means + stds, color=c, alpha=0.12, edgecolor='none', zorder=2)
+        if method not in legend_handles:
+            legend_handles[method] = line
+
+    ax.set_title(ds, fontsize=10, color=TEXT, pad=4)
+    ax.set_xlabel('Number of layers $L$', fontsize=9, color=TEXT)
+    ax.grid(axis='both', color=GRID, linewidth=0.6)
+    ax.set_axisbelow(True)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['left'].set_color('#C9CDD3')
+    ax.spines['bottom'].set_color('#C9CDD3')
+    ax.tick_params(colors=TEXT)
+    ax.set_xticks([3, 5, 10, 14, 18, 20])
+
+axes[0].set_ylabel('Test accuracy', fontsize=9, color=TEXT)
+
+handles = [legend_handles[m] for m in ['BP', 'DFA', 'DFA-GNN', 'GRAFT']]
+fig.tight_layout(rect=(0.0, 0.06, 1.0, 1.0), w_pad=1.5)
+# Display label: GRAFT data key stays for the lookup, render as KAFT
+fig.legend(handles, ['BP', 'DFA', 'DFA-GNN', 'KAFT'], frameon=False, loc='lower center',
+           ncol=4, bbox_to_anchor=(0.5, -0.005), handletextpad=0.6, columnspacing=1.8)
+
+fig.savefig('/home/yurenh2/graph-grape/kaft_realworld_depth.png', dpi=300, bbox_inches='tight')
+fig.savefig('/home/yurenh2/graph-grape/kaft_realworld_depth.pdf', bbox_inches='tight')
+plt.close(fig)
+print('Saved /home/yurenh2/graph-grape/kaft_realworld_depth.{png,pdf}')