{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 论文引用网络中的节点分类任务\n",
    "\n",
    "在这一教程中，我们将展示 GraphScope 如何结合图分析、图查询和图学习的能力，处理论文引用网络中的节点分类任务。\n",
    "\n",
    "\n",
    "在这个例子中我们使用的是 [ogbn-mag](https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag) 数据集。\"ogbn\" 是由微软学术关系图（Microsoft Academic Graph）的子集组成的异构图网络。该图中包含4种类型的实体（即论文、作者、机构和研究领域），以及连接两个实体的四种类型的有向关系边。\n",
    "\n",
    "我们需要处理的任务是，给出异构的 ogbn-mag 数据，在该图上预测每篇论文的类别。这是一个节点分类任务，该任务可以归类在各个领域、各个方向或研究小组的论文，通过对论文属性和引用图上的结构信息对论文进行分类。在该数据中，每个论文节点包含了一个从论文标题、摘要抽取的 128 维 word2vec 向量作为表征，该表征是经过预训练提前获取的；而结构信息是在计算过程中即时计算的。\n",
    "\n",
    "这一教程将会分为以下几个步骤：\n",
    "\n",
    "- 通过gremlin交互式查询图；\n",
    "- 执行图算法做图分析；\n",
    "- 执行基于图数据的机器学习任务；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install graphscope package if you are NOT in the Playground\n",
    "\n",
    "!pip3 install graphscope\n",
    "!pip3 uninstall -y importlib_metadata  # Address an module conflict issue on colab.google. Remove this line if you are not on colab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the graphscope module\n",
    "\n",
    "import graphscope\n",
    "\n",
    "graphscope.set_option(show_log=False)  # enable logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the obgn_mag dataset as a graph\n",
    "\n",
    "from graphscope.dataset import load_ogbn_mag\n",
    "\n",
    "graph = load_ogbn_mag()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactive query with gremlin\n",
    "\n",
    "在此示例中，我们启动了一个交互查询引擎，然后使用图遍历来查看两位给定作者共同撰写的论文数量。为了简化查询，我们假设作者可以分别由ID 2 和 4307 唯一标识。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the entrypoint for submitting Gremlin queries on graph g.\n",
    "interactive = graphscope.gremlin(graph)\n",
    "\n",
    "# Count the number of papers two authors (with id 2 and 4307) have co-authored.\n",
    "papers = interactive.execute(\n",
    "    \"g.V().has('author', 'id', 2).out('writes').where(__.in('writes').has('id', 4307)).count()\"\n",
    ").one()\n",
    "print(\"result\", papers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph analytics with analytical engine\n",
    "\n",
    "继续我们的示例，下面我们在图数据中进行图分析来生成节点结构特征。我们首先通过在特定周期内从全图中提取论文（使用Gremlin！）来导出一个子图，然后运行 k-core 分解和三角形计数以生成每个论文节点的结构特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exact a subgraph of publication within a time range.\n",
    "sub_graph = interactive.subgraph(\"g.V().has('year', inside(2014, 2020)).outE('cites')\")\n",
    "\n",
    "# Project the subgraph to simple graph by selecting papers and their citations.\n",
    "simple_g = sub_graph.project(vertices={\"paper\": []}, edges={\"cites\": []})\n",
    "# compute the kcore and triangle-counting.\n",
    "kc_result = graphscope.k_core(simple_g, k=5)\n",
    "tc_result = graphscope.triangles(simple_g)\n",
    "\n",
    "# Add the results as new columns to the citation graph.\n",
    "sub_graph = sub_graph.add_column(kc_result, {\"kcore\": \"r\"})\n",
    "sub_graph = sub_graph.add_column(tc_result, {\"tc\": \"r\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graph neural networks (GNNs)\n",
    "\n",
    "接着我们利用生成的结构特征和原有特征通过 GraphScope 的学习引擎来训练一个学习模型。\n",
    "\n",
    "在本例中，我们训练了 GCN 模型，将节点（论文）分类为349个类别，每个类别代表一个出处（例如预印本和会议）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the features for learning,\n",
    "# we chose original 128-dimension feature and k-core, triangle count result as new features.\n",
    "paper_features = []\n",
    "for i in range(128):\n",
    "    paper_features.append(\"feat_\" + str(i))\n",
    "paper_features.append(\"kcore\")\n",
    "paper_features.append(\"tc\")\n",
    "\n",
    "# Launch a learning engine. here we split the dataset, 75% as train, 10% as validation and 15% as test.\n",
    "lg = graphscope.graphlearn(\n",
    "    sub_graph,\n",
    "    nodes=[(\"paper\", paper_features)],\n",
    "    edges=[(\"paper\", \"cites\", \"paper\")],\n",
    "    gen_labels=[\n",
    "        (\"train\", \"paper\", 100, (0, 75)),\n",
    "        (\"val\", \"paper\", 100, (75, 85)),\n",
    "        (\"test\", \"paper\", 100, (85, 100)),\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "# Then we define the training process, use internal GCN model.\n",
    "from graphscope.learning.examples import GCN\n",
    "from graphscope.learning.graphlearn.python.model.tf.optimizer import get_tf_optimizer\n",
    "from graphscope.learning.graphlearn.python.model.tf.trainer import LocalTFTrainer\n",
    "\n",
    "\n",
    "def train(config, graph):\n",
    "    def model_fn():\n",
    "        return GCN(\n",
    "            graph,\n",
    "            config[\"class_num\"],\n",
    "            config[\"features_num\"],\n",
    "            config[\"batch_size\"],\n",
    "            val_batch_size=config[\"val_batch_size\"],\n",
    "            test_batch_size=config[\"test_batch_size\"],\n",
    "            categorical_attrs_desc=config[\"categorical_attrs_desc\"],\n",
    "            hidden_dim=config[\"hidden_dim\"],\n",
    "            in_drop_rate=config[\"in_drop_rate\"],\n",
    "            neighs_num=config[\"neighs_num\"],\n",
    "            hops_num=config[\"hops_num\"],\n",
    "            node_type=config[\"node_type\"],\n",
    "            edge_type=config[\"edge_type\"],\n",
    "            full_graph_mode=config[\"full_graph_mode\"],\n",
    "        )\n",
    "\n",
    "    trainer = LocalTFTrainer(\n",
    "        model_fn,\n",
    "        epoch=config[\"epoch\"],\n",
    "        optimizer=get_tf_optimizer(\n",
    "            config[\"learning_algo\"], config[\"learning_rate\"], config[\"weight_decay\"]\n",
    "        ),\n",
    "    )\n",
    "    trainer.train_and_evaluate()\n",
    "\n",
    "\n",
    "# hyperparameters config.\n",
    "config = {\n",
    "    \"class_num\": 349,  # output dimension\n",
    "    \"features_num\": 130,  # 128 dimension + kcore + triangle count\n",
    "    \"batch_size\": 500,\n",
    "    \"val_batch_size\": 100,\n",
    "    \"test_batch_size\": 100,\n",
    "    \"categorical_attrs_desc\": \"\",\n",
    "    \"hidden_dim\": 256,\n",
    "    \"in_drop_rate\": 0.5,\n",
    "    \"hops_num\": 2,\n",
    "    \"neighs_num\": [5, 10],\n",
    "    \"full_graph_mode\": False,\n",
    "    \"agg_type\": \"gcn\",  # mean, sum\n",
    "    \"learning_algo\": \"adam\",\n",
    "    \"learning_rate\": 0.01,\n",
    "    \"weight_decay\": 0.0005,\n",
    "    \"epoch\": 5,\n",
    "    \"node_type\": \"paper\",\n",
    "    \"edge_type\": \"cites\",\n",
    "}\n",
    "\n",
    "# Start traning and evaluating\n",
    "train(config, lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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