plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dependencies\n",
    "from modules.network import WordsNet, HashNet\n",
    "from modules.dataset.tweets import Tweets\n",
    "from modules.dataset.entities import Entities\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Climate action</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Reycling</th>\n",
       "      <th>Work life</th>\n",
       "      <th>Devt goals</th>\n",
       "      <th>Green economy</th>\n",
       "      <th>Intl politics</th>\n",
       "      <th>Digitalization</th>\n",
       "      <th>Pollution &amp; h</th>\n",
       "      <th>Lifestyle</th>\n",
       "      <th>Food</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Women</th>\n",
       "      <th>Green tech</th>\n",
       "      <th>Architecture</th>\n",
       "      <th>Misc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Climate action</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024878</td>\n",
       "      <td>0.026588</td>\n",
       "      <td>0.025563</td>\n",
       "      <td>0.019019</td>\n",
       "      <td>0.027454</td>\n",
       "      <td>0.040058</td>\n",
       "      <td>0.032426</td>\n",
       "      <td>0.027231</td>\n",
       "      <td>0.019981</td>\n",
       "      <td>0.026647</td>\n",
       "      <td>0.048836</td>\n",
       "      <td>0.020015</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>0.022391</td>\n",
       "      <td>0.014958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Nature</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017855</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>0.007481</td>\n",
       "      <td>0.016795</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>0.014820</td>\n",
       "      <td>0.014256</td>\n",
       "      <td>0.008927</td>\n",
       "      <td>0.013427</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.007629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Reycling</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.012443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011464</td>\n",
       "      <td>0.009193</td>\n",
       "      <td>0.018703</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.016987</td>\n",
       "      <td>0.012610</td>\n",
       "      <td>0.011887</td>\n",
       "      <td>0.013379</td>\n",
       "      <td>0.005373</td>\n",
       "      <td>0.009069</td>\n",
       "      <td>0.010713</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.005186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Work life</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>0.008665</td>\n",
       "      <td>0.009133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009674</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.007204</td>\n",
       "      <td>0.009881</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.008635</td>\n",
       "      <td>0.010236</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>0.010845</td>\n",
       "      <td>0.010730</td>\n",
       "      <td>0.010831</td>\n",
       "      <td>0.004189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Devt goals</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>0.005780</td>\n",
       "      <td>0.010260</td>\n",
       "      <td>0.010666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010541</td>\n",
       "      <td>0.010205</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.010213</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.008842</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.015547</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>0.010835</td>\n",
       "      <td>0.005354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Climate action    Nature  Reycling  Work life  Devt goals  \\\n",
       "Climate action        0.000000  0.024878  0.026588   0.025563    0.019019   \n",
       "Nature                0.008797  0.000000  0.017855   0.013252    0.007481   \n",
       "Reycling              0.009375  0.012443  0.000000   0.011464    0.009193   \n",
       "Work life             0.006364  0.008665  0.009133   0.000000    0.009674   \n",
       "Devt goals            0.006200  0.005780  0.010260   0.010666    0.000000   \n",
       "\n",
       "                Green economy  Intl politics  Digitalization  Pollution & h  \\\n",
       "Climate action       0.027454       0.040058        0.032426       0.027231   \n",
       "Nature               0.016795       0.012041        0.007982       0.012041   \n",
       "Reycling             0.018703       0.005936        0.016987       0.012610   \n",
       "Work life            0.010398       0.007204        0.009881       0.008347   \n",
       "Devt goals           0.010541       0.010205        0.011198       0.010213   \n",
       "\n",
       "                Lifestyle      Food  Australia     Women  Green tech  \\\n",
       "Climate action   0.019981  0.026647   0.048836  0.020015    0.046867   \n",
       "Nature           0.018607  0.014820   0.014256  0.008927    0.013427   \n",
       "Reycling         0.011887  0.013379   0.005373  0.009069    0.010713   \n",
       "Work life        0.008635  0.010236   0.006527  0.010845    0.010730   \n",
       "Devt goals       0.005875  0.008842   0.005786  0.015547    0.005788   \n",
       "\n",
       "                Architecture      Misc  \n",
       "Climate action      0.022391  0.014958  \n",
       "Nature              0.014884  0.007629  \n",
       "Reycling            0.010894  0.005186  \n",
       "Work life           0.010831  0.004189  \n",
       "Devt goals          0.010835  0.005354  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rels = pd.read_csv('data/final_comm_rels.csv',dtype=float)\n",
    "clusters = rels.columns.tolist()\n",
    "rels.index = map(lambda x: x.capitalize(), clusters)\n",
    "rels.columns = map(lambda x: x.capitalize(), clusters)\n",
    "rels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "\n",
    "ax.matshow(rels.drop('Misc',axis=1).values[:-1,:], cmap = plt.get_cmap('PuBu'))\n",
    "\n",
    "for PCM in ax.get_children():\n",
    "    if isinstance(PCM, plt.cm.ScalarMappable):\n",
    "        break\n",
    "plt.colorbar(PCM, ax=ax)\n",
    "\n",
    "xaxis = np.arange(len(clusters)-1)\n",
    "ax.set_xticks(xaxis)\n",
    "ax.set_yticks(xaxis)\n",
    "ax.set_xticklabels(rels.index[:-1])\n",
    "ax.set_yticklabels(rels.columns[:-1])\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('images/clusters_relations.png')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entity_text</th>\n",
       "      <th>community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#^#</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>#🎼</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>#1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>#1000solutions</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10286</td>\n",
       "      <td>#温暖化</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10287</td>\n",
       "      <td>#温暖化？</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10288</td>\n",
       "      <td>#石清水八幡宮</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10289</td>\n",
       "      <td>#開発と平和のためのスポーツの国際デー（4月6日）に寄せたメッセージ！#スポーツとsdgs</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10290</td>\n",
       "      <td>#髙田明</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         entity_text  community\n",
       "0                                                #^#          6\n",
       "1                                                 #🎼          8\n",
       "2                                          #0urworld         16\n",
       "3                                                 #1         16\n",
       "4                                     #1000solutions         15\n",
       "...                                              ...        ...\n",
       "10286                                           #温暖化         16\n",
       "10287                                          #温暖化？         16\n",
       "10288                                        #石清水八幡宮         16\n",
       "10289  #開発と平和のためのスポーツの国際デー（4月6日）に寄せたメッセージ！#スポーツとsdgs          4\n",
       "10290                                           #髙田明          4\n",
       "\n",
       "[10291 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hash_comm = pd.read_csv('data/hashtags_comm.csv')\n",
    "hash_comm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[791,\n",
       " 767,\n",
       " 619,\n",
       " 541,\n",
       " 494,\n",
       " 462,\n",
       " 405,\n",
       " 405,\n",
       " 380,\n",
       " 380,\n",
       " 377,\n",
       " 305,\n",
       " 296,\n",
       " 295,\n",
       " 246,\n",
       " 3528]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_commAll = []\n",
    "groups=hash_comm.groupby(by='community')\n",
    "for g in groups:\n",
    "    card_commAll.append(g[1].shape[0])\n",
    "card_commAll"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "TWEETS_DB_PATH = 'data/db/tweets.json'  # Path to tweets dataset\n",
    "HASHTAGS_DB_PATH = 'data/db/hashtags.json'  # Path to hashtags dataset\n",
    "# Define networks container, indexed by periods\n",
    "networks = {\n",
    "    2017: None,\n",
    "    \n",
    "    2018: None,\n",
    "    2019: None\n",
    "}\n",
    "# Import tweets dataset\n",
    "tweets = Tweets()\n",
    "tweets.from_json(TWEETS_DB_PATH)\n",
    "# Import hashtags dataset\n",
    "hashtags = Entities()\n",
    "hashtags.from_json(HASHTAGS_DB_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>entity_index</th>\n",
       "      <th>entity_text</th>\n",
       "      <th>entity_tag</th>\n",
       "      <th>entity_conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1101574442575167489</td>\n",
       "      <td>0</td>\n",
       "      <td>#Humans</td>\n",
       "      <td>#</td>\n",
       "      <td>0.6243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1101574442575167489</td>\n",
       "      <td>7</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>^</td>\n",
       "      <td>0.4925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1101574446341607424</td>\n",
       "      <td>27</td>\n",
       "      <td>#ClimateChange</td>\n",
       "      <td>#</td>\n",
       "      <td>0.6968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1101574446341607424</td>\n",
       "      <td>36</td>\n",
       "      <td>#ActOnClimate</td>\n",
       "      <td>#</td>\n",
       "      <td>0.7929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1101574446341607424</td>\n",
       "      <td>37</td>\n",
       "      <td>#climate</td>\n",
       "      <td>#</td>\n",
       "      <td>0.9559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id  entity_index     entity_text entity_tag  entity_conf\n",
       "0  1101574442575167489             0         #Humans          #       0.6243\n",
       "1  1101574442575167489             7  #climatechange          ^       0.4925\n",
       "2  1101574446341607424            27  #ClimateChange          #       0.6968\n",
       "3  1101574446341607424            36   #ActOnClimate          #       0.7929\n",
       "4  1101574446341607424            37        #climate          #       0.9559"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hashtags.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hash_net = HashNet.from_entities(hashtags)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#0urWorld</td>\n",
       "      <td>#ClimateChange</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>#0urWorld</td>\n",
       "      <td>#Environment</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#ClimateChange</td>\n",
       "      <td>#15MARCH</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>#ClimateChange</td>\n",
       "      <td>#15andFairness</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>#ClimateChange</td>\n",
       "      <td>#15march</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           source          target  weight\n",
       "0       #0urWorld  #ClimateChange       1\n",
       "1       #0urWorld    #Environment       1\n",
       "2  #ClimateChange        #15MARCH       2\n",
       "3  #ClimateChange  #15andFairness       2\n",
       "4  #ClimateChange        #15march       1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edgelist = nx.to_pandas_edgelist(hash_net.net)\n",
    "edgelist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>entity_text</th>\n",
       "      <th>community</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>1</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>#environment</td>\n",
       "      <td>1</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#15march</td>\n",
       "      <td>2</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#15andfairness</td>\n",
       "      <td>2</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#15march</td>\n",
       "      <td>1</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           source          target  weight     entity_text  community\n",
       "0       #0urworld  #climatechange       1       #0urworld         16\n",
       "1       #0urworld    #environment       1       #0urworld         16\n",
       "2  #climatechange        #15march       2  #climatechange         16\n",
       "3  #climatechange  #15andfairness       2  #climatechange         16\n",
       "4  #climatechange        #15march       1  #climatechange         16"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edgelist.source = edgelist.source.apply(lambda x: x.lower())\n",
    "edgelist.target = edgelist.target.apply(lambda x: x.lower())\n",
    "tmp=pd.merge(edgelist, hash_comm, left_on='source', right_on='entity_text')\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>community_s</th>\n",
       "      <th>community_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#0urworld</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>92</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77992</td>\n",
       "      <td>#abc</td>\n",
       "      <td>#depletedresources</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77993</td>\n",
       "      <td>#civilunrest</td>\n",
       "      <td>#depletedresources</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77994</td>\n",
       "      <td>#climateactionnow</td>\n",
       "      <td>#depletedresources</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77995</td>\n",
       "      <td>#schoolstrike4climate</td>\n",
       "      <td>#pacificclimatewarriors</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77996</td>\n",
       "      <td>#alternativefacts</td>\n",
       "      <td>#billion</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77997 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      source                   target  weight  community_s  \\\n",
       "0                  #0urworld           #climatechange       1           16   \n",
       "1             #climatechange           #climatechange      92           16   \n",
       "2             #climatechange           #climatechange      13           16   \n",
       "3             #climatechange           #climatechange       2           16   \n",
       "4             #climatechange           #climatechange      30           16   \n",
       "...                      ...                      ...     ...          ...   \n",
       "77992                   #abc       #depletedresources       1            4   \n",
       "77993           #civilunrest       #depletedresources       1            4   \n",
       "77994      #climateactionnow       #depletedresources       1            1   \n",
       "77995  #schoolstrike4climate  #pacificclimatewarriors       1            1   \n",
       "77996      #alternativefacts                 #billion       1           14   \n",
       "\n",
       "       community_t  \n",
       "0               16  \n",
       "1               16  \n",
       "2               16  \n",
       "3               16  \n",
       "4               16  \n",
       "...            ...  \n",
       "77992            4  \n",
       "77993            4  \n",
       "77994            4  \n",
       "77995            1  \n",
       "77996           14  \n",
       "\n",
       "[77997 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp2=pd.merge(tmp, hash_comm, left_on='target', right_on='entity_text',suffixes=('_s','_t')\n",
    "             ).drop(['entity_text_s','entity_text_t'],axis=1)\n",
    "\n",
    "tmp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>community_s</th>\n",
       "      <th>community_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>#geoengineering</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>#geoengineering</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>#srm</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>#renewables</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>#climateaction</td>\n",
       "      <td>#climatechange</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77952</td>\n",
       "      <td>#power</td>\n",
       "      <td>#wiretapping</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77953</td>\n",
       "      <td>#control</td>\n",
       "      <td>#wiretapping</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77958</td>\n",
       "      <td>#globalwarming</td>\n",
       "      <td>#universalbasicincome</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77986</td>\n",
       "      <td>#drones</td>\n",
       "      <td>#climatechange’s</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77994</td>\n",
       "      <td>#climateactionnow</td>\n",
       "      <td>#depletedresources</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40458 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  source                 target  weight  community_s  \\\n",
       "9        #geoengineering         #climatechange       1            2   \n",
       "10       #geoengineering         #climatechange       2            2   \n",
       "13                  #srm         #climatechange       1            2   \n",
       "15           #renewables         #climatechange       1            1   \n",
       "16        #climateaction         #climatechange       5            1   \n",
       "...                  ...                    ...     ...          ...   \n",
       "77952             #power           #wiretapping       1            1   \n",
       "77953           #control           #wiretapping       1            4   \n",
       "77958     #globalwarming  #universalbasicincome       1           16   \n",
       "77986            #drones       #climatechange’s       1            8   \n",
       "77994  #climateactionnow     #depletedresources       1            1   \n",
       "\n",
       "       community_t  \n",
       "9               16  \n",
       "10              16  \n",
       "13              16  \n",
       "15              16  \n",
       "16              16  \n",
       "...            ...  \n",
       "77952           14  \n",
       "77953           14  \n",
       "77958            4  \n",
       "77986           16  \n",
       "77994            4  \n",
       "\n",
       "[40458 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_ext=tmp2[tmp2.community_s != tmp2.community_t]\n",
    "edges_ext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>community_s</th>\n",
       "      <th>community_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>#fridaysforfuture</td>\n",
       "      <td>#1o5c</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>#climatestrike</td>\n",
       "      <td>#1o5c</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>448</td>\n",
       "      <td>#science</td>\n",
       "      <td>#adeliepenguins</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>879</td>\n",
       "      <td>#waterislife</td>\n",
       "      <td>#amazonia</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>972</td>\n",
       "      <td>#science</td>\n",
       "      <td>#animalrights</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75560</td>\n",
       "      <td>#energy</td>\n",
       "      <td>#erice</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75897</td>\n",
       "      <td>#energy</td>\n",
       "      <td>#materials</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76134</td>\n",
       "      <td>#energy</td>\n",
       "      <td>#sicily</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77179</td>\n",
       "      <td>#waterislife</td>\n",
       "      <td>#wellbeingmatters</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77194</td>\n",
       "      <td>#waterislife</td>\n",
       "      <td>#whalesarepeopletoo</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  source               target  weight  community_s  \\\n",
       "148    #fridaysforfuture                #1o5c       2            1   \n",
       "151       #climatestrike                #1o5c       4            1   \n",
       "448             #science      #adeliepenguins       1            1   \n",
       "879         #waterislife            #amazonia       1            1   \n",
       "972             #science        #animalrights       1            1   \n",
       "...                  ...                  ...     ...          ...   \n",
       "75560            #energy               #erice       1            1   \n",
       "75897            #energy           #materials       1            1   \n",
       "76134            #energy              #sicily       1            1   \n",
       "77179       #waterislife    #wellbeingmatters       1            1   \n",
       "77194       #waterislife  #whalesarepeopletoo       1            1   \n",
       "\n",
       "       community_t  \n",
       "148              2  \n",
       "151              2  \n",
       "448              2  \n",
       "879              2  \n",
       "972              2  \n",
       "...            ...  \n",
       "75560            2  \n",
       "75897            2  \n",
       "76134            2  \n",
       "77179            2  \n",
       "77194            2  \n",
       "\n",
       "[342 rows x 5 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = edges_ext.groupby(by=['community_s','community_t'])\n",
    "nodes=[]\n",
    "nodes_id=[]\n",
    "for group in groups:\n",
    "    nodes_id.append(group[0])\n",
    "    nodes.append( group[1])\n",
    "nodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_ext = list(map(lambda x: np.sum(x.weight.values), nodes))\n",
    "links_ext = nodes_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source  target  weights\n",
       "0       1       2      454\n",
       "1       1       3      619\n",
       "2       1       4      589\n",
       "3       1       5      239\n",
       "4       1       6      458"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = pd.DataFrame.from_records(links_ext, columns=['source','target'])\n",
    "net['weights']=weights_ext\n",
    "net.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.to_csv('data/comm_net_all.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entity_text</th>\n",
       "      <th>membership</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>#1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>#abpoli</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>#Activism</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>#ActNow</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>#actonclimate</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     entity_text  membership  ID\n",
       "0             #1          16   1\n",
       "1        #abpoli           1   2\n",
       "2      #Activism          16   3\n",
       "3        #ActNow          16   4\n",
       "4  #actonclimate           1   5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm17nodes = pd.read_csv('data/community_2017_nodes.csv')\n",
    "comm17nodes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[175, 199, 115, 158, 123, 111, 169, 69, 114, 100, 109, 89, 77, 53, 63, 1393]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "card_comm17 = []\n",
    "groups=comm17nodes.groupby(by='membership')\n",
    "for g in groups:\n",
    "    card_comm17.append(g[1].shape[0])\n",
    "card_comm17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.241385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.091385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.337860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.040137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source  Target   Weights\n",
       "0     1.0     1.0  0.241385\n",
       "1   115.0     1.0  0.116711\n",
       "2  2634.0     1.0  0.091385\n",
       "3     2.0     2.0  0.337860\n",
       "4    41.0     2.0  0.040137"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm17edges = pd.read_csv('data/community_2017_edges.csv',dtype=float)\n",
    "comm17edges.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weights</th>\n",
       "      <th>entity_text</th>\n",
       "      <th>membership</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.241385</td>\n",
       "      <td>#1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.046684</td>\n",
       "      <td>#1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>0.091385</td>\n",
       "      <td>#1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116711</td>\n",
       "      <td>#GeoEngineering</td>\n",
       "      <td>2</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>115.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>#GeoEngineering</td>\n",
       "      <td>2</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Source  Target   Weights      entity_text  membership   ID\n",
       "0     1.0     1.0  0.241385               #1          16    1\n",
       "1     1.0   115.0  0.046684               #1          16    1\n",
       "2     1.0  2634.0  0.091385               #1          16    1\n",
       "3   115.0     1.0  0.116711  #GeoEngineering           2  115\n",
       "4   115.0    57.0  0.000279  #GeoEngineering           2  115"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp=pd.merge(comm17edges, comm17nodes, left_on='Source', right_on='ID')\n",
    "tmp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weights</th>\n",
       "      <th>membership_s</th>\n",
       "      <th>membership_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.241385</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116711</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.091385</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.046684</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>115.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.251190</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21158</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>2890.0</td>\n",
       "      <td>0.345273</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21159</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>0.345273</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21160</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21161</td>\n",
       "      <td>2896.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21162</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>2923.0</td>\n",
       "      <td>0.345273</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21163 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Source  Target   Weights  membership_s  membership_t\n",
       "0         1.0     1.0  0.241385            16            16\n",
       "1       115.0     1.0  0.116711             2            16\n",
       "2      2634.0     1.0  0.091385             2            16\n",
       "3         1.0   115.0  0.046684            16             2\n",
       "4       115.0   115.0  0.251190             2             2\n",
       "...       ...     ...       ...           ...           ...\n",
       "21158  2890.0  2890.0  0.345273            16            16\n",
       "21159  2896.0  2896.0  0.345273            11            11\n",
       "21160  2923.0  2896.0  0.195273            11            11\n",
       "21161  2896.0  2923.0  0.195273            11            11\n",
       "21162  2923.0  2923.0  0.345273            11            11\n",
       "\n",
       "[21163 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp2=pd.merge(tmp, comm17nodes, left_on='Target', right_on='ID',suffixes=('_s','_t')\n",
    "             ).drop(['entity_text_s','entity_text_t','ID_s','ID_t'],axis=1)\n",
    "\n",
    "tmp2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weights</th>\n",
       "      <th>membership_s</th>\n",
       "      <th>membership_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.116711</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.091385</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.046684</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>57.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0.141663</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2634.0</td>\n",
       "      <td>0.091385</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21024</td>\n",
       "      <td>1664.0</td>\n",
       "      <td>1828.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21085</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2173.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21086</td>\n",
       "      <td>2173.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21152</td>\n",
       "      <td>2603.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21153</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>2603.0</td>\n",
       "      <td>0.195273</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8640 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Source  Target   Weights  membership_s  membership_t\n",
       "1       115.0     1.0  0.116711             2            16\n",
       "2      2634.0     1.0  0.091385             2            16\n",
       "3         1.0   115.0  0.046684            16             2\n",
       "6        57.0   115.0  0.141663            16             2\n",
       "7         1.0  2634.0  0.091385            16             2\n",
       "...       ...     ...       ...           ...           ...\n",
       "21024  1664.0  1828.0  0.195273            10            16\n",
       "21085  2320.0  2173.0  0.195273            16            12\n",
       "21086  2173.0  2320.0  0.195273            12            16\n",
       "21152  2603.0  2600.0  0.195273            16            11\n",
       "21153  2600.0  2603.0  0.195273            11            16\n",
       "\n",
       "[8640 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edges_ext17=tmp2[tmp2.membership_s != tmp2.membership_t]\n",
    "edges_ext17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>Weights</th>\n",
       "      <th>membership_s</th>\n",
       "      <th>membership_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1780</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.004837</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2125</td>\n",
       "      <td>199.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.015072</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2445</td>\n",
       "      <td>188.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.023289</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2452</td>\n",
       "      <td>93.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>0.011896</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2832</td>\n",
       "      <td>199.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.015621</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2833</td>\n",
       "      <td>208.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.001224</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2834</td>\n",
       "      <td>186.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.001332</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2900</td>\n",
       "      <td>1079.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6257</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2083.0</td>\n",
       "      <td>0.041536</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6591</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2481.0</td>\n",
       "      <td>0.028847</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6673</td>\n",
       "      <td>1043.0</td>\n",
       "      <td>2615.0</td>\n",
       "      <td>0.035732</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6913</td>\n",
       "      <td>227.0</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>0.007601</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8721</td>\n",
       "      <td>828.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.005044</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9777</td>\n",
       "      <td>199.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>0.030334</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9838</td>\n",
       "      <td>199.0</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>0.051834</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12264</td>\n",
       "      <td>2039.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>0.007046</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12266</td>\n",
       "      <td>93.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>0.006539</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12276</td>\n",
       "      <td>1231.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>0.007693</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12280</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>775.0</td>\n",
       "      <td>0.005334</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12566</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2257.0</td>\n",
       "      <td>0.014769</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12611</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>0.058074</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14260</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>0.044459</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14490</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>0.017197</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14520</td>\n",
       "      <td>2119.0</td>\n",
       "      <td>3091.0</td>\n",
       "      <td>0.050759</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14555</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>0.032797</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14599</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>1540.0</td>\n",
       "      <td>0.032797</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14620</td>\n",
       "      <td>2373.0</td>\n",
       "      <td>3037.0</td>\n",
       "      <td>0.032797</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Source  Target   Weights  membership_s  membership_t\n",
       "1780   2373.0    15.0  0.004837             1             2\n",
       "2125    199.0    81.0  0.015072             1             2\n",
       "2445    188.0   111.0  0.023289             1             2\n",
       "2452     93.0   111.0  0.011896             1             2\n",
       "2832    199.0   156.0  0.015621             1             2\n",
       "2833    208.0   156.0  0.001224             1             2\n",
       "2834    186.0   156.0  0.001332             1             2\n",
       "2900   1079.0   156.0  0.000520             1             2\n",
       "6257    199.0  2083.0  0.041536             1             2\n",
       "6591    199.0  2481.0  0.028847             1             2\n",
       "6673   1043.0  2615.0  0.035732             1             2\n",
       "6913    227.0  2824.0  0.007601             1             2\n",
       "8721    828.0   150.0  0.005044             1             2\n",
       "9777    199.0   363.0  0.030334             1             2\n",
       "9838    199.0  1846.0  0.051834             1             2\n",
       "12264  2039.0   775.0  0.007046             1             2\n",
       "12266    93.0   775.0  0.006539             1             2\n",
       "12276  1231.0   775.0  0.007693             1             2\n",
       "12280  1067.0   775.0  0.005334             1             2\n",
       "12566    93.0  2257.0  0.014769             1             2\n",
       "12611    93.0  2999.0  0.058074             1             2\n",
       "14260    13.0  1738.0  0.044459             1             2\n",
       "14490  2119.0  1454.0  0.017197             1             2\n",
       "14520  2119.0  3091.0  0.050759             1             2\n",
       "14555  2373.0   475.0  0.032797             1             2\n",
       "14599  2373.0  1540.0  0.032797             1             2\n",
       "14620  2373.0  3037.0  0.032797             1             2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = edges_ext17.groupby(by=['membership_s','membership_t'])\n",
    "nodes=[]\n",
    "nodes_id=[]\n",
    "for group in groups:\n",
    "    nodes_id.append(group[0])\n",
    "    nodes.append( group[1])\n",
    "nodes[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights_ext17 = list(map(lambda x: np.sum(x.Weights.values), nodes))\n",
    "links_ext17 = nodes_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.584980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.267604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.722635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.127212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.467756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   source  target   weights\n",
       "0       1       2  0.584980\n",
       "1       1       3  0.267604\n",
       "2       1       4  0.722635\n",
       "3       1       5  0.127212\n",
       "4       1       6  0.467756"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = pd.DataFrame.from_records(links_ext17, columns=['source','target'])\n",
    "net['weights']=weights_ext17\n",
    "net.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "net.to_csv('data/comm_net_2017.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm = np.sort(comm17nodes.membership.drop_duplicates().values)\n",
    "comm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Climate action', 'Nature', 'Reycling', 'Work life', 'Devt goals',\n",
       "       'Green economy', 'Intl politics', 'Digitalization', 'Pollution & h',\n",
       "       'Lifestyle', 'Food', 'Australia', 'Women', 'Green tech', 'Architecture',\n",
       "       'Misc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rels.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>ID</th>\n",
       "      <th>card_all</th>\n",
       "      <th>card_2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Climate action</td>\n",
       "      <td>1</td>\n",
       "      <td>791</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Nature</td>\n",
       "      <td>2</td>\n",
       "      <td>767</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Reycling</td>\n",
       "      <td>3</td>\n",
       "      <td>619</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Work life</td>\n",
       "      <td>4</td>\n",
       "      <td>541</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Devt goals</td>\n",
       "      <td>5</td>\n",
       "      <td>494</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Green economy</td>\n",
       "      <td>6</td>\n",
       "      <td>462</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Intl politics</td>\n",
       "      <td>7</td>\n",
       "      <td>405</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Digitalization</td>\n",
       "      <td>8</td>\n",
       "      <td>405</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Pollution &amp; h</td>\n",
       "      <td>9</td>\n",
       "      <td>380</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Lifestyle</td>\n",
       "      <td>10</td>\n",
       "      <td>380</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Food</td>\n",
       "      <td>11</td>\n",
       "      <td>377</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Australia</td>\n",
       "      <td>12</td>\n",
       "      <td>305</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Women</td>\n",
       "      <td>13</td>\n",
       "      <td>296</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Green tech</td>\n",
       "      <td>14</td>\n",
       "      <td>295</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Architecture</td>\n",
       "      <td>15</td>\n",
       "      <td>246</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Misc</td>\n",
       "      <td>16</td>\n",
       "      <td>3528</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             label  ID  card_all  card_2017\n",
       "0   Climate action   1       791        175\n",
       "1           Nature   2       767        199\n",
       "2         Reycling   3       619        115\n",
       "3        Work life   4       541        158\n",
       "4       Devt goals   5       494        123\n",
       "5    Green economy   6       462        111\n",
       "6    Intl politics   7       405        169\n",
       "7   Digitalization   8       405         69\n",
       "8    Pollution & h   9       380        114\n",
       "9        Lifestyle  10       380        100\n",
       "10            Food  11       377        109\n",
       "11       Australia  12       305         89\n",
       "12           Women  13       296         77\n",
       "13      Green tech  14       295         53\n",
       "14    Architecture  15       246         63\n",
       "15            Misc  16      3528       1393"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm_ID = pd.DataFrame([], columns=['label','ID'])\n",
    "comm_ID.ID = comm\n",
    "comm_ID['card_all'] = card_commAll\n",
    "comm_ID['card_2017'] = card_comm17\n",
    "comm_ID.label=rels.index\n",
    "comm_ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "comm_ID.to_csv('data/comm_ids.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024878</td>\n",
       "      <td>0.026588</td>\n",
       "      <td>0.025563</td>\n",
       "      <td>0.019019</td>\n",
       "      <td>0.027454</td>\n",
       "      <td>0.040058</td>\n",
       "      <td>0.032426</td>\n",
       "      <td>0.027231</td>\n",
       "      <td>0.019981</td>\n",
       "      <td>0.026647</td>\n",
       "      <td>0.048836</td>\n",
       "      <td>0.020015</td>\n",
       "      <td>0.046867</td>\n",
       "      <td>0.022391</td>\n",
       "      <td>0.014958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.008797</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017855</td>\n",
       "      <td>0.013252</td>\n",
       "      <td>0.007481</td>\n",
       "      <td>0.016795</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.007982</td>\n",
       "      <td>0.012041</td>\n",
       "      <td>0.018607</td>\n",
       "      <td>0.014820</td>\n",
       "      <td>0.014256</td>\n",
       "      <td>0.008927</td>\n",
       "      <td>0.013427</td>\n",
       "      <td>0.014884</td>\n",
       "      <td>0.007629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.012443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.011464</td>\n",
       "      <td>0.009193</td>\n",
       "      <td>0.018703</td>\n",
       "      <td>0.005936</td>\n",
       "      <td>0.016987</td>\n",
       "      <td>0.012610</td>\n",
       "      <td>0.011887</td>\n",
       "      <td>0.013379</td>\n",
       "      <td>0.005373</td>\n",
       "      <td>0.009069</td>\n",
       "      <td>0.010713</td>\n",
       "      <td>0.010894</td>\n",
       "      <td>0.005186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.006364</td>\n",
       "      <td>0.008665</td>\n",
       "      <td>0.009133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009674</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.007204</td>\n",
       "      <td>0.009881</td>\n",
       "      <td>0.008347</td>\n",
       "      <td>0.008635</td>\n",
       "      <td>0.010236</td>\n",
       "      <td>0.006527</td>\n",
       "      <td>0.010845</td>\n",
       "      <td>0.010730</td>\n",
       "      <td>0.010831</td>\n",
       "      <td>0.004189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.006200</td>\n",
       "      <td>0.005780</td>\n",
       "      <td>0.010260</td>\n",
       "      <td>0.010666</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010541</td>\n",
       "      <td>0.010205</td>\n",
       "      <td>0.011198</td>\n",
       "      <td>0.010213</td>\n",
       "      <td>0.005875</td>\n",
       "      <td>0.008842</td>\n",
       "      <td>0.005786</td>\n",
       "      <td>0.015547</td>\n",
       "      <td>0.005788</td>\n",
       "      <td>0.010835</td>\n",
       "      <td>0.005354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.006130</td>\n",
       "      <td>0.008589</td>\n",
       "      <td>0.012593</td>\n",
       "      <td>0.008858</td>\n",
       "      <td>0.005973</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007023</td>\n",
       "      <td>0.009966</td>\n",
       "      <td>0.008322</td>\n",
       "      <td>0.011525</td>\n",
       "      <td>0.006769</td>\n",
       "      <td>0.004348</td>\n",
       "      <td>0.007909</td>\n",
       "      <td>0.008621</td>\n",
       "      <td>0.015437</td>\n",
       "      <td>0.003845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.008138</td>\n",
       "      <td>0.006628</td>\n",
       "      <td>0.004955</td>\n",
       "      <td>0.007791</td>\n",
       "      <td>0.004876</td>\n",
       "      <td>0.007286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007836</td>\n",
       "      <td>0.006488</td>\n",
       "      <td>0.004868</td>\n",
       "      <td>0.005103</td>\n",
       "      <td>0.008873</td>\n",
       "      <td>0.005250</td>\n",
       "      <td>0.008614</td>\n",
       "      <td>0.004493</td>\n",
       "      <td>0.004345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>0.002922</td>\n",
       "      <td>0.001864</td>\n",
       "      <td>0.007839</td>\n",
       "      <td>0.003175</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.004275</td>\n",
       "      <td>0.001643</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002241</td>\n",
       "      <td>0.002779</td>\n",
       "      <td>0.003412</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>0.003920</td>\n",
       "      <td>0.005548</td>\n",
       "      <td>0.005724</td>\n",
       "      <td>0.001637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.005728</td>\n",
       "      <td>0.008250</td>\n",
       "      <td>0.009883</td>\n",
       "      <td>0.009541</td>\n",
       "      <td>0.008333</td>\n",
       "      <td>0.008904</td>\n",
       "      <td>0.012789</td>\n",
       "      <td>0.004937</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008339</td>\n",
       "      <td>0.007145</td>\n",
       "      <td>0.006348</td>\n",
       "      <td>0.004959</td>\n",
       "      <td>0.008939</td>\n",
       "      <td>0.012431</td>\n",
       "      <td>0.003488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.002573</td>\n",
       "      <td>0.007556</td>\n",
       "      <td>0.004233</td>\n",
       "      <td>0.004461</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.005037</td>\n",
       "      <td>0.001768</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003860</td>\n",
       "      <td>0.003314</td>\n",
       "      <td>0.001825</td>\n",
       "      <td>0.003529</td>\n",
       "      <td>0.009330</td>\n",
       "      <td>0.001934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.004240</td>\n",
       "      <td>0.008767</td>\n",
       "      <td>0.008488</td>\n",
       "      <td>0.006381</td>\n",
       "      <td>0.006766</td>\n",
       "      <td>0.006468</td>\n",
       "      <td>0.004067</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.006634</td>\n",
       "      <td>0.009306</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004468</td>\n",
       "      <td>0.007004</td>\n",
       "      <td>0.006085</td>\n",
       "      <td>0.007761</td>\n",
       "      <td>0.003501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.008191</td>\n",
       "      <td>0.006023</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.004614</td>\n",
       "      <td>0.002399</td>\n",
       "      <td>0.002367</td>\n",
       "      <td>0.007100</td>\n",
       "      <td>0.002592</td>\n",
       "      <td>0.005313</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.002807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003516</td>\n",
       "      <td>0.005521</td>\n",
       "      <td>0.001680</td>\n",
       "      <td>0.002713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.002650</td>\n",
       "      <td>0.003328</td>\n",
       "      <td>0.006052</td>\n",
       "      <td>0.009143</td>\n",
       "      <td>0.003824</td>\n",
       "      <td>0.005344</td>\n",
       "      <td>0.004396</td>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.002274</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.003223</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>0.007053</td>\n",
       "      <td>0.002162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.006245</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.006002</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>0.003346</td>\n",
       "      <td>0.005988</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>0.008776</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.005175</td>\n",
       "      <td>0.005053</td>\n",
       "      <td>0.003902</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009502</td>\n",
       "      <td>0.002742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>0.003161</td>\n",
       "      <td>0.003692</td>\n",
       "      <td>0.004451</td>\n",
       "      <td>0.002832</td>\n",
       "      <td>0.005591</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.003366</td>\n",
       "      <td>0.005643</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>0.003020</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.003701</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.022482</td>\n",
       "      <td>0.030260</td>\n",
       "      <td>0.023617</td>\n",
       "      <td>0.023007</td>\n",
       "      <td>0.023663</td>\n",
       "      <td>0.026448</td>\n",
       "      <td>0.026180</td>\n",
       "      <td>0.023303</td>\n",
       "      <td>0.019746</td>\n",
       "      <td>0.023508</td>\n",
       "      <td>0.019210</td>\n",
       "      <td>0.022105</td>\n",
       "      <td>0.017654</td>\n",
       "      <td>0.025975</td>\n",
       "      <td>0.024133</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4         5         6         7   \\\n",
       "1   0.000000  0.024878  0.026588  0.025563  0.019019  0.027454  0.040058   \n",
       "2   0.008797  0.000000  0.017855  0.013252  0.007481  0.016795  0.012041   \n",
       "3   0.009375  0.012443  0.000000  0.011464  0.009193  0.018703  0.005936   \n",
       "4   0.006364  0.008665  0.009133  0.000000  0.009674  0.010398  0.007204   \n",
       "5   0.006200  0.005780  0.010260  0.010666  0.000000  0.010541  0.010205   \n",
       "6   0.006130  0.008589  0.012593  0.008858  0.005973  0.000000  0.007023   \n",
       "7   0.008138  0.006628  0.004955  0.007791  0.004876  0.007286  0.000000   \n",
       "8   0.002922  0.001864  0.007839  0.003175  0.005987  0.004275  0.001643   \n",
       "9   0.005728  0.008250  0.009883  0.009541  0.008333  0.008904  0.012789   \n",
       "10  0.002573  0.007556  0.004233  0.004461  0.002633  0.005037  0.001768   \n",
       "11  0.004240  0.008767  0.008488  0.006381  0.006766  0.006468  0.004067   \n",
       "12  0.008191  0.006023  0.002292  0.004614  0.002399  0.002367  0.007100   \n",
       "13  0.002205  0.002650  0.003328  0.006052  0.009143  0.003824  0.005344   \n",
       "14  0.006245  0.006374  0.006002  0.003462  0.003346  0.005988  0.005703   \n",
       "15  0.001549  0.003161  0.003692  0.004451  0.002832  0.005591  0.002097   \n",
       "16  0.022482  0.030260  0.023617  0.023007  0.023663  0.026448  0.026180   \n",
       "\n",
       "          8         9         10        11        12        13        14  \\\n",
       "1   0.032426  0.027231  0.019981  0.026647  0.048836  0.020015  0.046867   \n",
       "2   0.007982  0.012041  0.018607  0.014820  0.014256  0.008927  0.013427   \n",
       "3   0.016987  0.012610  0.011887  0.013379  0.005373  0.009069  0.010713   \n",
       "4   0.009881  0.008347  0.008635  0.010236  0.006527  0.010845  0.010730   \n",
       "5   0.011198  0.010213  0.005875  0.008842  0.005786  0.015547  0.005788   \n",
       "6   0.009966  0.008322  0.011525  0.006769  0.004348  0.007909  0.008621   \n",
       "7   0.007836  0.006488  0.004868  0.005103  0.008873  0.005250  0.008614   \n",
       "8   0.000000  0.002241  0.002779  0.003412  0.000857  0.003920  0.005548   \n",
       "9   0.004937  0.000000  0.008339  0.007145  0.006348  0.004959  0.008939   \n",
       "10  0.003231  0.004762  0.000000  0.003860  0.003314  0.001825  0.003529   \n",
       "11  0.007663  0.006634  0.009306  0.000000  0.004468  0.007004  0.006085   \n",
       "12  0.002592  0.005313  0.003746  0.002807  0.000000  0.003516  0.005521   \n",
       "13  0.004396  0.003117  0.002274  0.004398  0.003223  0.000000  0.001673   \n",
       "14  0.008776  0.004988  0.005175  0.005053  0.003902  0.002906  0.000000   \n",
       "15  0.003366  0.005643  0.003644  0.003020  0.000743  0.001801  0.003701   \n",
       "16  0.023303  0.019746  0.023508  0.019210  0.022105  0.017654  0.025975   \n",
       "\n",
       "          15        16  \n",
       "1   0.022391  0.014958  \n",
       "2   0.014884  0.007629  \n",
       "3   0.010894  0.005186  \n",
       "4   0.010831  0.004189  \n",
       "5   0.010835  0.005354  \n",
       "6   0.015437  0.003845  \n",
       "7   0.004493  0.004345  \n",
       "8   0.005724  0.001637  \n",
       "9   0.012431  0.003488  \n",
       "10  0.009330  0.001934  \n",
       "11  0.007761  0.003501  \n",
       "12  0.001680  0.002713  \n",
       "13  0.007053  0.002162  \n",
       "14  0.009502  0.002742  \n",
       "15  0.000000  0.001325  \n",
       "16  0.024133  0.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rels = pd.read_csv('data/final_comm_rels.csv',dtype=float)\n",
    "rels.columns = comm\n",
    "rels.index = comm\n",
    "rels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>0.024133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>0.025975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>0.017654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0.022105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>0.019210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>115</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>117</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>119</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     source  target    weight\n",
       "0        16      15  0.024133\n",
       "1        16      14  0.025975\n",
       "2        16      13  0.017654\n",
       "3        16      12  0.022105\n",
       "4        16      11  0.019210\n",
       "..      ...     ...       ...\n",
       "115       4       2  0.008665\n",
       "116       4       1  0.006364\n",
       "117       3       2  0.012443\n",
       "118       3       1  0.009375\n",
       "119       2       1  0.008797\n",
       "\n",
       "[120 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depend_net = nx.from_pandas_adjacency(rels)\n",
    "depend_edgelist = nx.to_pandas_edgelist(depend_net)\n",
    "depend_edgelist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "depend_edgelist.to_csv('data/comm_net_interdep.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}