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, 191 insertions, 0 deletions

diff --git a/figures/gen_fig4_combined.py b/figures/gen_fig4_combined.py
new file mode 100644
index 0000000..5b8d464
--- /dev/null
+++ b/figures/gen_fig4_combined.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+"""Figure 4-style combined plot: 4 panels (depth / add / remove / flip).
+
+Each panel: 9 curves = 3 datasets × 3 methods.
+  color    = dataset (Cora / CiteSeer / PubMed)
+  linestyle = method  (BP dashed, DFA-GNN dotted, GRAFT solid)
+
+Matches DFA-GNN Figure 4 layout.
+"""
+
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import to_rgba
+from matplotlib.lines import Line2D
+
+DATASETS = ['Cora', 'CiteSeer', 'PubMed']
+METHODS = ['BP', 'DFA-GNN', 'GRAFT']      # data-lookup keys (unchanged)
+DISPLAY_NAME = {'BP': 'BP', 'DFA-GNN': 'DFA-GNN', 'GRAFT': 'KAFT'}
+
+DEPTHS = [4, 6, 8, 10, 12, 14, 16, 18, 20]
+RATES = [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
+ATTACKS = ['add', 'remove', 'flip']
+
+# Method colors — consistent with other GRAFT figures
+METHOD_COLORS = {
+    'BP':      '#888888',  # gray
+    'DFA-GNN': '#3B7AC2',  # complementary blue
+    'GRAFT':   '#C23B3B',  # brick red (ours)
+}
+# Dataset linestyles
+DS_STYLE = {
+    'Cora':     (0, ()),          # solid
+    'CiteSeer': (0, (5, 2)),      # dashed
+    'PubMed':   (0, (1, 1.5)),    # dotted
+}
+DS_MARKER = {
+    'Cora':     'o',
+    'CiteSeer': 's',
+    'PubMed':   '^',
+}
+
+GRID_COLOR = '#ECEFF3'
+TEXT_COLOR = '#2F3437'
+
+# --- depth data sources (depth_sweep reuses gen_depth_sweep_fig loaders) -----
+DEPTH_SOURCES = [
+    'results/combo_20seeds/per_seed_data.json',
+    'results/hero_extras_20seeds/per_seed_data.json',
+    'results/shallow_depth_20seeds/per_seed_data.json',
+    'results/bp_graft_depth_20seeds/per_seed_data.json',
+    'results/dfagnn_depth_20seeds/per_seed_data.json',
+    'results/dfagnn_resgcn_20seeds/per_seed_data.json',
+    'results/depth_extras_20seeds/per_seed_data.json',  # L=14, 18
+]
+PERTURB_SOURCE = 'results/perturb_sweep_20seeds/per_seed_data.json'
+
+
+def load_depth():
+    merged = {}
+    for path in DEPTH_SOURCES:
+        try:
+            with open(f'/home/yurenh2/graph-grape/{path}') as f:
+                d = json.load(f)
+            for k, v in d.items():
+                if k not in merged:
+                    merged[k] = v
+                else:
+                    for sk, sv in v.items():
+                        if sk not in merged[k]:
+                            merged[k][sk] = sv
+        except FileNotFoundError:
+            pass
+    return merged
+
+
+def depth_lookup(data, ds, L, method):
+    for key in [f'{ds}_L{L}_{method}', f'{ds}_{method}' if L == 6 else None]:
+        if key and key in data and len(data[key]) >= 15:
+            vals = np.array(list(data[key].values())) * 100
+            return vals.mean(), vals.std()
+    return None
+
+
+def perturb_lookup(data, ds, attack, rate, method):
+    key = f'{ds}_{attack}_r{rate}_{method}'
+    if key in data and len(data[key]) >= 15:
+        vals = np.array(list(data[key].values())) * 100
+        return vals.mean(), vals.std()
+    return None
+
+
+def plot_panel(ax, panel_type, data, title):
+    """panel_type: 'depth' or attack name."""
+    xs = DEPTHS if panel_type == 'depth' else RATES
+    for ds in DATASETS:
+        for method in METHODS:
+            means = []
+            stds = []
+            xs_used = []
+            for x in xs:
+                if panel_type == 'depth':
+                    r = depth_lookup(data, ds, x, method)
+                else:
+                    r = perturb_lookup(data, ds, panel_type, x, method)
+                if r is not None:
+                    xs_used.append(x)
+                    means.append(r[0])
+                    stds.append(r[1])
+            if not means:
+                continue
+            color = METHOD_COLORS[method]
+            style = DS_STYLE[ds]
+            marker = DS_MARKER[ds]
+            ax.plot(xs_used, means, color=color, linestyle=style, marker=marker,
+                     markersize=4.5, linewidth=1.3,
+                     markerfacecolor=to_rgba(color, alpha=0.35),
+                     markeredgecolor=color, markeredgewidth=0.7,
+                     zorder=3)
+            # Shaded band (light)
+            means = np.array(means); stds = np.array(stds)
+            ax.fill_between(xs_used, means - stds, means + stds,
+                             color=color, alpha=0.06, edgecolor='none', zorder=1)
+
+    ax.set_title(title, fontsize=10, color=TEXT_COLOR, pad=5)
+    ax.grid(axis='both', color=GRID_COLOR, 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_COLOR)
+    if panel_type == 'depth':
+        ax.set_xticks(DEPTHS)
+        ax.set_xlabel('Number of layers $L$', fontsize=9, color=TEXT_COLOR)
+    else:
+        ax.set_xticks(RATES)
+        ax.set_xlabel('Perturbation rate $\\lambda$', fontsize=9, color=TEXT_COLOR)
+
+
+def main():
+    depth_data = load_depth()
+    with open(f'/home/yurenh2/graph-grape/{PERTURB_SOURCE}') as f:
+        perturb_data = json.load(f)
+
+    plt.rcParams.update({
+        'font.size': 9,
+        'axes.labelsize': 9,
+        'xtick.labelsize': 8,
+        'ytick.labelsize': 8,
+        'legend.fontsize': 8,
+        'pdf.fonttype': 42,
+        'ps.fonttype': 42,
+    })
+
+    fig, axes = plt.subplots(1, 4, figsize=(13.5, 3.2))
+
+    plot_panel(axes[0], 'depth',  depth_data,   '(a) Depth')
+    plot_panel(axes[1], 'add',    perturb_data, '(b) Add')
+    plot_panel(axes[2], 'remove', perturb_data, '(c) Remove')
+    plot_panel(axes[3], 'flip',   perturb_data, '(d) Flip')
+
+    axes[0].set_ylabel('Test accuracy (%)', fontsize=9, color=TEXT_COLOR)
+
+    # Dual-legend: colors (methods) + linestyles (datasets)
+    method_handles = [Line2D([0], [0], color=METHOD_COLORS[m], linewidth=2.5,
+                              label=DISPLAY_NAME[m])
+                      for m in METHODS]
+    ds_handles = [Line2D([0], [0], color='#444', linestyle=DS_STYLE[ds],
+                          marker=DS_MARKER[ds], markersize=4.5,
+                          linewidth=1.5, label=ds)
+                  for ds in DATASETS]
+
+    fig.tight_layout(rect=(0.0, 0.09, 1.0, 1.0), w_pad=1.3)
+    fig.legend(handles=method_handles, loc='lower left', bbox_to_anchor=(0.08, -0.01),
+               frameon=False, ncol=3, handletextpad=0.5, columnspacing=1.5,
+               title='Method', title_fontsize=9)
+    fig.legend(handles=ds_handles, loc='lower right', bbox_to_anchor=(0.92, -0.01),
+               frameon=False, ncol=3, handletextpad=0.5, columnspacing=1.5,
+               title='Dataset', title_fontsize=9)
+
+    fig.savefig('/home/yurenh2/graph-grape/kaft_fig4_combined.png',
+                dpi=300, bbox_inches='tight')
+    fig.savefig('/home/yurenh2/graph-grape/kaft_fig4_combined.pdf',
+                bbox_inches='tight')
+    plt.close(fig)
+    print('Saved /home/yurenh2/graph-grape/kaft_fig4_combined.{png,pdf}')
+
+
+if __name__ == '__main__':
+    main()