{
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  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construct interaction graphs\n",
    "\n",
    "Constructing interaction graphs and counting the neighbour interactions for each user."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the dataset and create user nodes dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "case = '...'\n",
    "casegraphs = '...'\n",
    "\n",
    "df_data = pd.read_csv(f'../sentiment-analysis/{case}.csv')\n",
    "user_nodes = df_data.groupby('user')['id'].apply(list).to_dict()\n",
    "\n",
    "df_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct and export nearest-neighbour graph and count nearest-neighbours in nearest-neighbour graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "# Define undirected graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Construct nearest-neighbour graph\n",
    "for _, row in df_data.iterrows():\n",
    "    G.add_node(row['id'], user=row['user'], sentiment=row['s'], text=row['text'])\n",
    "    if row['parentid'] != 'no_parent':\n",
    "        G.add_edge(row['id'], row['parentid'])\n",
    "\n",
    "# Export nearest-neighbour graph\n",
    "nx.write_graphml(G, f'{casegraphs}-nn-graph.graphml')\n",
    "\n",
    "# Define nearest-neighbour interaction count dictionary\n",
    "nn_count = {}\n",
    "\n",
    "# Count nearest neighbours for each user\n",
    "for node in G.nodes(data=True):\n",
    "    node_id, attributes = node\n",
    "    user = attributes['user']\n",
    "\n",
    "    count = len(set(G.neighbors(node_id)) - set(user_nodes[user]))\n",
    "\n",
    "    if user in nn_count:\n",
    "        nn_count[user] += count\n",
    "    else:\n",
    "        nn_count[user] = count"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct and export transitive-closure graph and count nearest-neighbours in transitive-closure graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct transitive-closure graph\n",
    "tc_G = nx.transitive_closure(G)\n",
    "\n",
    "# Export transitive-closure graph\n",
    "nx.write_graphml(tc_G, f'{casegraphs}-tc-graph.graphml')\n",
    "\n",
    "# Define transitive-closure interaction count dictionary\n",
    "tc_count = {}\n",
    "\n",
    "# Count neighbours for each user\n",
    "for node in tc_G.nodes(data=True):\n",
    "    node_id, attributes = node\n",
    "    user = attributes['user']\n",
    "\n",
    "    count = len(set(tc_G.neighbors(node_id)) - set(user_nodes[user]))\n",
    "\n",
    "    if user in tc_count:\n",
    "        tc_count[user] += count\n",
    "    else:\n",
    "        tc_count[user] = count"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct and export directed-transitive-closure graph and count nearest-neighbours in directed-transitive-closure graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define directed graph\n",
    "dG = nx.DiGraph()\n",
    "\n",
    "# Construct nearest-neighbour directed graph\n",
    "for _, row in df_data.iterrows():\n",
    "    dG.add_node(row['id'], user=row['user'], sentiment=row['s'], text=row['text'])\n",
    "    if row['parentid'] != 'no_parent':\n",
    "        dG.add_edge(row['id'], row['parentid'])\n",
    "\n",
    "nx.write_graphml(dG, f'{casegraphs}-nn-dgraph.graphml')\n",
    "\n",
    "# Construct directed-transitive-closure directed graph\n",
    "dtc_dG = nx.transitive_closure(dG)\n",
    "\n",
    "# Export directed-transitive-closure directed graph\n",
    "nx.write_graphml(dtc_dG, f'{casegraphs}-dtc-dgraph.graphml')\n",
    "\n",
    "# Convert to directed-transitive-closure undirected graph\n",
    "dtc_G = dtc_dG.to_undirected()\n",
    "\n",
    "# Export directed-transitive-closure graph\n",
    "nx.write_graphml(dtc_G, f'{casegraphs}-dtc-graph.graphml')\n",
    "\n",
    "# Define directed-transitive-closure interaction count dictionary\n",
    "dtc_count = {}\n",
    "\n",
    "# Count neighbours for each user\n",
    "for node in dtc_G.nodes(data=True):\n",
    "    node_id, attributes = node\n",
    "    user = attributes['user']\n",
    "\n",
    "    count = len(set(dtc_G.neighbors(node_id)) - set(user_nodes[user]))\n",
    "\n",
    "    if user in dtc_count:\n",
    "        dtc_count[user] += count\n",
    "    else:\n",
    "        dtc_count[user] = count"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct and export user interaction count dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_c = df_data.groupby('user')['id'].count().to_dict()\n",
    "user_s = df_data.groupby('user')['s'].mean().to_dict()\n",
    "user_stds = df_data.groupby('user')['s'].std().to_dict()\n",
    "\n",
    "df_users = pd.DataFrame({\n",
    "    'nn-count': pd.Series(nn_count), \n",
    "    'dtc-count': pd.Series(dtc_count), \n",
    "    'tc-count': pd.Series(tc_count),\n",
    "    'mean-sentiment': pd.Series(user_s),\n",
    "    'std-sentiment': pd.Series(user_stds),\n",
    "    'post-count': pd.Series(user_c)\n",
    "})\n",
    "\n",
    "export_dataset = f'{case}-users.csv'\n",
    "df_users.to_csv(export_dataset, index=False)\n",
    "\n",
    "df_users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_users.describe()"
   ]
  }
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