{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "frontmatter",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"GCNs on the Cora Citation Network\"\n",
    "category: \"Deep Learning\"\n",
    "tags:\n",
    "  - deep-learning\n",
    "  - graph-neural-networks\n",
    "  - gcn\n",
    "  - node-classification\n",
    "  - semi-supervised-learning\n",
    "  - cora\n",
    "date: 2026-07-31\n",
    "status: evergreen\n",
    "description: \"Compare a feature-only MLP with graph convolution and an edge-rewiring ablation on Cora under a low-label node-classification split.\"\n",
    "sources:\n",
    "  - https://arxiv.org/abs/1609.02907\n",
    "  - https://arxiv.org/abs/1603.08861\n",
    "  - https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz\n",
    "notebook_page: false\n",
    "---\n",
    "\n",
    "## Question\n",
    "\n",
    "Can citation edges improve paper-topic classification when only 20 papers per class are labeled?\n",
    "\n",
    "Cora makes the comparison concrete. Each node is a machine-learning paper represented by a binary bag-of-words vector. An edge records a citation. The target is one of seven research topics. A feature-only multilayer perceptron sees each abstract vocabulary vector in isolation. A graph convolutional network can mix that vector with cited and citing papers before classifying it.\n",
    "\n",
    "The experiment also trains the same GCN on an endpoint-shuffled graph. That null keeps the feature matrix, labels, edge count, and directed source/destination degree sequences before duplicate removal. Its lost semantic structure helps separate the value of meaningful neighborhoods from extra parameters or smoothing alone.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "setup",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-01T04:04:11.646360Z",
     "iopub.status.busy": "2026-08-01T04:04:11.646145Z",
     "iopub.status.idle": "2026-08-01T04:04:12.507547Z",
     "shell.execute_reply": "2026-08-01T04:04:12.507060Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'python': '3.12.13', 'torch': '2.13.0', 'numpy': '2.5.1', 'platform': 'macOS-26.5-arm64-arm-64bit', 'device': 'cpu'}\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import platform\n",
    "import random\n",
    "import sys\n",
    "import tarfile\n",
    "import time\n",
    "import warnings\n",
    "from copy import deepcopy\n",
    "from pathlib import Path\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "os.environ.setdefault(\"TOKENIZERS_PARALLELISM\", \"false\")\n",
    "plt.style.use(\"seaborn-v0_8-whitegrid\")\n",
    "\n",
    "SPLIT_SEED = 7\n",
    "MODEL_SEEDS = [0, 1, 2, 3, 4]\n",
    "random.seed(SPLIT_SEED)\n",
    "np.random.seed(SPLIT_SEED)\n",
    "torch.set_num_threads(min(4, os.cpu_count() or 1))\n",
    "\n",
    "\n",
    "def find_repo_root() -> Path:\n",
    "    for candidate in (Path.cwd(), *Path.cwd().parents):\n",
    "        if (candidate / \"content\").is_dir() and (candidate / \"package.json\").exists():\n",
    "            return candidate\n",
    "    raise RuntimeError(\"Run the notebook from inside the notes repository\")\n",
    "\n",
    "\n",
    "REPO_ROOT = find_repo_root()\n",
    "DATA_DIR = REPO_ROOT / \"work\" / \"notebook-data\" / \"cora\"\n",
    "DEVICE = torch.device(\"cpu\")\n",
    "print({\n",
    "    \"python\": sys.version.split()[0],\n",
    "    \"torch\": torch.__version__,\n",
    "    \"numpy\": np.__version__,\n",
    "    \"platform\": platform.platform(),\n",
    "    \"device\": str(DEVICE),\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "provenance",
   "metadata": {},
   "source": [
    "## Data and Provenance\n",
    "\n",
    "The [LINQS Cora archive](https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz) contains 2,708 papers, 1,433 binary word indicators, seven topic labels, and 5,429 citation records. The notebook downloads that archive into the ignored `work/notebook-data/cora/` directory. No downloaded data enters Git.\n",
    "\n",
    "The split follows the low-label scale used by [Planetoid](https://arxiv.org/abs/1603.08861) and the [GCN paper](https://arxiv.org/abs/1609.02907): 20 training labels per class, 500 validation nodes, and 1,000 test nodes. This notebook draws one deterministic stratified training set and then samples validation and test nodes from the remainder. Its split is reproducible, though it is not the serialized Planetoid public split, so its accuracy should not be compared point-for-point with the paper's table.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "load-data",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-01T04:04:12.509275Z",
     "iopub.status.busy": "2026-08-01T04:04:12.509096Z",
     "iopub.status.idle": "2026-08-01T04:04:12.940800Z",
     "shell.execute_reply": "2026-08-01T04:04:12.940204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'nodes': 2708, 'citation_records': 5429, 'features': 1433, 'classes': 7, 'train/validation/test': (140, 500, 1000), 'citation_label_agreement': 0.814}\n"
     ]
    }
   ],
   "source": [
    "CORA_URL = \"https://linqs-data.soe.ucsc.edu/public/lbc/cora.tgz\"\n",
    "ARCHIVE = DATA_DIR / \"cora.tgz\"\n",
    "CONTENT = DATA_DIR / \"cora\" / \"cora.content\"\n",
    "CITES = DATA_DIR / \"cora\" / \"cora.cites\"\n",
    "\n",
    "if not CONTENT.exists() or not CITES.exists():\n",
    "    DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
    "    if not ARCHIVE.exists():\n",
    "        urlretrieve(CORA_URL, ARCHIVE)\n",
    "    with tarfile.open(ARCHIVE, \"r:gz\") as bundle:\n",
    "        bundle.extractall(DATA_DIR, filter=\"data\")\n",
    "\n",
    "paper_ids = []\n",
    "feature_rows = []\n",
    "label_names = []\n",
    "with CONTENT.open(encoding=\"utf-8\") as handle:\n",
    "    for line in handle:\n",
    "        fields = line.split()\n",
    "        paper_ids.append(fields[0])\n",
    "        feature_rows.append([int(value) for value in fields[1:-1]])\n",
    "        label_names.append(fields[-1])\n",
    "\n",
    "node_for_id = {paper_id: index for index, paper_id in enumerate(paper_ids)}\n",
    "classes = sorted(set(label_names))\n",
    "class_for_name = {name: index for index, name in enumerate(classes)}\n",
    "labels_np = np.array([class_for_name[name] for name in label_names], dtype=np.int64)\n",
    "features_np = np.asarray(feature_rows, dtype=np.float32)\n",
    "features_np /= np.maximum(features_np.sum(axis=1, keepdims=True), 1.0)\n",
    "\n",
    "citation_pairs = []\n",
    "with CITES.open(encoding=\"utf-8\") as handle:\n",
    "    for line in handle:\n",
    "        cited, citing = line.split()\n",
    "        if cited in node_for_id and citing in node_for_id:\n",
    "            citation_pairs.append((node_for_id[cited], node_for_id[citing]))\n",
    "\n",
    "citations_np = np.asarray(citation_pairs, dtype=np.int64).T\n",
    "edges_np = np.concatenate([citations_np, citations_np[::-1]], axis=1)\n",
    "num_nodes, num_features = features_np.shape\n",
    "num_classes = len(classes)\n",
    "\n",
    "rng = np.random.default_rng(SPLIT_SEED)\n",
    "train_parts = []\n",
    "for class_id in range(num_classes):\n",
    "    candidates = np.flatnonzero(labels_np == class_id)\n",
    "    train_parts.append(rng.choice(candidates, size=20, replace=False))\n",
    "train_idx_np = np.sort(np.concatenate(train_parts))\n",
    "remaining = np.setdiff1d(np.arange(num_nodes), train_idx_np)\n",
    "rng.shuffle(remaining)\n",
    "val_idx_np = np.sort(remaining[:500])\n",
    "test_idx_np = np.sort(remaining[500:1500])\n",
    "\n",
    "x = torch.tensor(features_np, dtype=torch.float32, device=DEVICE)\n",
    "y = torch.tensor(labels_np, dtype=torch.long, device=DEVICE)\n",
    "train_idx = torch.tensor(train_idx_np, dtype=torch.long, device=DEVICE)\n",
    "val_idx = torch.tensor(val_idx_np, dtype=torch.long, device=DEVICE)\n",
    "test_idx = torch.tensor(test_idx_np, dtype=torch.long, device=DEVICE)\n",
    "\n",
    "edge_homophily = float(\n",
    "    (labels_np[citations_np[0]] == labels_np[citations_np[1]]).mean()\n",
    ")\n",
    "print({\n",
    "    \"nodes\": num_nodes,\n",
    "    \"citation_records\": citations_np.shape[1],\n",
    "    \"features\": num_features,\n",
    "    \"classes\": num_classes,\n",
    "    \"train/validation/test\": (len(train_idx), len(val_idx), len(test_idx)),\n",
    "    \"citation_label_agreement\": round(edge_homophily, 3),\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "method",
   "metadata": {},
   "source": [
    "## Models and Ablation\n",
    "\n",
    "For adjacency matrix (A), add self-loops and normalize with\n",
    "\n",
    "$$\n",
    "\\hat{A} = \\tilde{D}^{-1/2}(A + I)\\tilde{D}^{-1/2}.\n",
    "$$\n",
    "\n",
    "The two-layer GCN computes\n",
    "\n",
    "$$\n",
    "Z = \\hat{A}\\,\\operatorname{Dropout}\\left(\\operatorname{ReLU}(\\hat{A}XW_0)\\right)W_1.\n",
    "$$\n",
    "\n",
    "The MLP removes both multiplications by (hat{A}) while retaining the same width, nonlinearity, dropout, optimizer, and labeled nodes. The rewired GCN permutes edge destinations before symmetrization and normalization. It still propagates the same node features through a graph of similar density, but citations no longer point toward their observed papers.\n",
    "\n",
    "Full-batch training uses Adam, cross-entropy on the 140 labeled nodes, validation-based early stopping, and five initialization seeds. The test labels remain untouched until each seed's best validation checkpoint has been restored.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "graph-models",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-01T04:04:12.942350Z",
     "iopub.status.busy": "2026-08-01T04:04:12.942222Z",
     "iopub.status.idle": "2026-08-01T04:04:12.968511Z",
     "shell.execute_reply": "2026-08-01T04:04:12.968150Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'observed_edge_label_agreement': 0.814, 'rewired_edge_label_agreement': 0.171}\n"
     ]
    }
   ],
   "source": [
    "def normalized_adjacency(edge_index: np.ndarray, n: int) -> torch.Tensor:\n",
    "    loops = np.arange(n, dtype=np.int64)\n",
    "    indices_np = np.concatenate(\n",
    "        [edge_index, np.stack([loops, loops])], axis=1\n",
    "    )\n",
    "    indices = torch.tensor(indices_np, dtype=torch.long, device=DEVICE)\n",
    "    raw = torch.sparse_coo_tensor(\n",
    "        indices,\n",
    "        torch.ones(indices.shape[1], device=DEVICE),\n",
    "        (n, n),\n",
    "    ).coalesce()\n",
    "    indices = raw.indices()\n",
    "    values = torch.ones(indices.shape[1], device=DEVICE)\n",
    "    degree = torch.zeros(n, device=DEVICE)\n",
    "    degree.scatter_add_(0, indices[0], values)\n",
    "    norm = degree[indices[0]].rsqrt() * degree[indices[1]].rsqrt()\n",
    "    return torch.sparse_coo_tensor(indices, norm, (n, n)).coalesce()\n",
    "\n",
    "\n",
    "def shuffled_edges(edge_index: np.ndarray, seed: int) -> np.ndarray:\n",
    "    rng = np.random.default_rng(seed)\n",
    "    shuffled = edge_index.copy()\n",
    "    shuffled[1] = rng.permutation(shuffled[1])\n",
    "    return shuffled[:, shuffled[0] != shuffled[1]]\n",
    "\n",
    "\n",
    "adjacency = normalized_adjacency(edges_np, num_nodes)\n",
    "rewired_edges_np = shuffled_edges(edges_np, seed=101)\n",
    "rewired_adjacency = normalized_adjacency(rewired_edges_np, num_nodes)\n",
    "x_aggregated = torch.sparse.mm(adjacency, x)\n",
    "x_rewired = torch.sparse.mm(rewired_adjacency, x)\n",
    "\n",
    "rewired_homophily = float(\n",
    "    (labels_np[rewired_edges_np[0]] == labels_np[rewired_edges_np[1]]).mean()\n",
    ")\n",
    "print({\n",
    "    \"observed_edge_label_agreement\": round(edge_homophily, 3),\n",
    "    \"rewired_edge_label_agreement\": round(rewired_homophily, 3),\n",
    "})\n",
    "\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, d_in: int, d_hidden: int, d_out: int):\n",
    "        super().__init__()\n",
    "        self.input = nn.Linear(d_in, d_hidden)\n",
    "        self.output = nn.Linear(d_hidden, d_out)\n",
    "\n",
    "    def encode(self, features: torch.Tensor) -> torch.Tensor:\n",
    "        return F.relu(self.input(features))\n",
    "\n",
    "    def forward(self, features: torch.Tensor) -> torch.Tensor:\n",
    "        hidden = F.dropout(self.encode(features), p=0.5, training=self.training)\n",
    "        return self.output(hidden)\n",
    "\n",
    "\n",
    "class GCN(nn.Module):\n",
    "    def __init__(self, d_in: int, d_hidden: int, d_out: int, graph: torch.Tensor):\n",
    "        super().__init__()\n",
    "        self.input = nn.Linear(d_in, d_hidden)\n",
    "        self.output = nn.Linear(d_hidden, d_out)\n",
    "        self.graph = graph\n",
    "\n",
    "    def encode(self, aggregated_features: torch.Tensor) -> torch.Tensor:\n",
    "        return F.relu(self.input(aggregated_features))\n",
    "\n",
    "    def forward(self, aggregated_features: torch.Tensor) -> torch.Tensor:\n",
    "        hidden = F.dropout(\n",
    "            self.encode(aggregated_features), p=0.5, training=self.training\n",
    "        )\n",
    "        hidden = torch.sparse.mm(self.graph, hidden)\n",
    "        return self.output(hidden)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "training",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-01T04:04:12.969960Z",
     "iopub.status.busy": "2026-08-01T04:04:12.969844Z",
     "iopub.status.idle": "2026-08-01T04:04:19.564505Z",
     "shell.execute_reply": "2026-08-01T04:04:19.563958Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP (features only)    test accuracy=0.543 +/- 0.028 epochs=212\n",
      "GCN (citations)        test accuracy=0.773 +/- 0.008 epochs=201\n",
      "GCN (rewired edges)    test accuracy=0.195 +/- 0.022 epochs=133\n",
      "total training time: 6.6s\n"
     ]
    }
   ],
   "source": [
    "def accuracy(logits: torch.Tensor, indices: torch.Tensor) -> float:\n",
    "    return float((logits[indices].argmax(dim=1) == y[indices]).float().mean())\n",
    "\n",
    "\n",
    "def fit(model: nn.Module, inputs: torch.Tensor, seed: int):\n",
    "    torch.manual_seed(seed)\n",
    "    model.reset_parameters() if hasattr(model, \"reset_parameters\") else None\n",
    "    for module in model.modules():\n",
    "        if isinstance(module, nn.Linear):\n",
    "            module.reset_parameters()\n",
    "\n",
    "    optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "    best_state = None\n",
    "    best_val = -1.0\n",
    "    wait = 0\n",
    "    history = {\"loss\": [], \"train\": [], \"validation\": []}\n",
    "\n",
    "    for epoch in range(400):\n",
    "        model.train()\n",
    "        optimizer.zero_grad()\n",
    "        logits = model(inputs)\n",
    "        loss = F.cross_entropy(logits[train_idx], y[train_idx])\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            logits = model(inputs)\n",
    "            train_accuracy = accuracy(logits, train_idx)\n",
    "            val_accuracy = accuracy(logits, val_idx)\n",
    "        history[\"loss\"].append(float(loss.detach()))\n",
    "        history[\"train\"].append(train_accuracy)\n",
    "        history[\"validation\"].append(val_accuracy)\n",
    "\n",
    "        if val_accuracy > best_val + 1e-5:\n",
    "            best_val = val_accuracy\n",
    "            best_state = deepcopy(model.state_dict())\n",
    "            wait = 0\n",
    "        else:\n",
    "            wait += 1\n",
    "        if wait >= 50:\n",
    "            break\n",
    "\n",
    "    model.load_state_dict(best_state)\n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        logits = model(inputs)\n",
    "    return {\n",
    "        \"test_accuracy\": accuracy(logits, test_idx),\n",
    "        \"best_validation_accuracy\": best_val,\n",
    "        \"epochs\": len(history[\"loss\"]),\n",
    "        \"history\": history,\n",
    "        \"logits\": logits,\n",
    "        \"model\": model,\n",
    "    }\n",
    "\n",
    "\n",
    "configurations = {\n",
    "    \"MLP (features only)\": lambda: (\n",
    "        MLP(num_features, 16, num_classes).to(DEVICE), x\n",
    "    ),\n",
    "    \"GCN (citations)\": lambda: (\n",
    "        GCN(num_features, 16, num_classes, adjacency).to(DEVICE), x_aggregated\n",
    "    ),\n",
    "    \"GCN (rewired edges)\": lambda: (\n",
    "        GCN(num_features, 16, num_classes, rewired_adjacency).to(DEVICE), x_rewired\n",
    "    ),\n",
    "}\n",
    "\n",
    "started = time.perf_counter()\n",
    "runs = {name: [] for name in configurations}\n",
    "for name, factory in configurations.items():\n",
    "    for seed in MODEL_SEEDS:\n",
    "        model, inputs = factory()\n",
    "        runs[name].append(fit(model, inputs, seed))\n",
    "elapsed = time.perf_counter() - started\n",
    "\n",
    "for name, model_runs in runs.items():\n",
    "    scores = np.array([run[\"test_accuracy\"] for run in model_runs])\n",
    "    epochs = np.array([run[\"epochs\"] for run in model_runs])\n",
    "    print(\n",
    "        f\"{name:22s} test accuracy={scores.mean():.3f} +/- {scores.std(ddof=1):.3f} \"\n",
    "        f\"epochs={epochs.mean():.0f}\"\n",
    "    )\n",
    "print(f\"total training time: {elapsed:.1f}s\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "analysis-plan",
   "metadata": {},
   "source": [
    "## Reading the Result\n",
    "\n",
    "The bar chart summarizes variability across initialization seeds. Learning curves come from seed 0 and show the validation signal used for checkpoint selection. The embedding plot projects the GCN's 16-dimensional hidden states with PCA; labels color points for interpretation only and do not enter PCA.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "plots",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-01T04:04:19.565760Z",
     "iopub.status.busy": "2026-08-01T04:04:19.565596Z",
     "iopub.status.idle": "2026-08-01T04:04:19.855414Z",
     "shell.execute_reply": "2026-08-01T04:04:19.854784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x420 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = list(runs)\n",
    "means = [np.mean([run[\"test_accuracy\"] for run in runs[name]]) for name in names]\n",
    "stds = [np.std([run[\"test_accuracy\"] for run in runs[name]], ddof=1) for name in names]\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.2))\n",
    "colors = [\"#777777\", \"#cf5c36\", \"#5f8f8b\"]\n",
    "axes[0].bar(names, means, yerr=stds, capsize=5, color=colors)\n",
    "axes[0].set_ylim(0.35, 0.9)\n",
    "axes[0].set_ylabel(\"Test accuracy\")\n",
    "axes[0].set_title(\"Five initialization seeds\")\n",
    "axes[0].tick_params(axis=\"x\", rotation=15)\n",
    "\n",
    "for name, color in zip(names, colors):\n",
    "    curve = runs[name][0][\"history\"][\"validation\"]\n",
    "    axes[1].plot(curve, label=name, color=color, linewidth=2)\n",
    "axes[1].set_xlabel(\"Epoch\")\n",
    "axes[1].set_ylabel(\"Validation accuracy\")\n",
    "axes[1].set_title(\"Early-stopping trajectories (seed 0)\")\n",
    "axes[1].legend(frameon=False)\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "best_gcn = max(runs[\"GCN (citations)\"], key=lambda run: run[\"best_validation_accuracy\"])\n",
    "with torch.no_grad():\n",
    "    hidden = best_gcn[\"model\"].encode(x_aggregated).cpu()\n",
    "centered = hidden - hidden.mean(dim=0, keepdim=True)\n",
    "_, _, components = torch.pca_lowrank(centered, q=2)\n",
    "projected = (centered @ components).numpy()\n",
    "\n",
    "plt.figure(figsize=(7.5, 5.5))\n",
    "for class_id, class_name in enumerate(classes):\n",
    "    mask = labels_np == class_id\n",
    "    plt.scatter(\n",
    "        projected[mask, 0],\n",
    "        projected[mask, 1],\n",
    "        s=11,\n",
    "        alpha=0.55,\n",
    "        label=class_name,\n",
    "    )\n",
    "plt.xlabel(\"Hidden-state PC 1\")\n",
    "plt.ylabel(\"Hidden-state PC 2\")\n",
    "plt.title(\"GCN hidden states on Cora\")\n",
    "plt.legend(markerscale=2, fontsize=8, frameon=False, ncol=2)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interpretation",
   "metadata": {},
   "source": [
    "## Interpretation\n",
    "\n",
    "Across five initialization seeds, the citation GCN reached mean test accuracy $0.773 \\pm 0.008$, where the interval is one sample standard deviation. The feature-only MLP reached $0.543 \\pm 0.028$. The 23.0 percentage-point gap measures the value of neighborhood propagation for this fixed low-label split and implementation.\n",
    "\n",
    "Observed citation endpoints shared a topic 81.4% of the time. Endpoint shuffling reduced that agreement to 17.1%, and the same GCN architecture then reached $0.195 \\pm 0.022$ test accuracy. Smoothing through arbitrary neighbors destroyed useful signal. These measurements apply to a homophilous citation graph. They do not establish performance on heterophilous graphs, inductive graph tasks, or datasets with abundant labels.\n",
    "\n",
    "## Environment and Reproduction\n",
    "\n",
    "The environment cell records the exact Python, PyTorch, NumPy, platform, and device values stored with this run. Data downloads to `work/notebook-data/cora/`. From the repository root, execute and publish with:\n",
    "\n",
    "```sh\n",
    ".venv/bin/python scripts/execute_notebooks.py content/ml/deep-learning/graph-neural-networks.ipynb\n",
    "npm run render:notebooks\n",
    "npm run build\n",
    "```\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.12 (notes)",
   "language": "python",
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