Hanyu_Chen_Social_Network_Basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "from zipfile import ZipFile\n",
    "import urllib.request as urllib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://www-personal.umich.edu/~mejn/netdata/dolphins.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with urllib.urlopen(url) as stream:\n",
    "    with ZipFile(BytesIO(stream.read())) as archive:\n",
    "        txt = archive.read('dolphins.txt').decode()\n",
    "        gml = archive.read('dolphins.gml').decode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creator \"Mark Newman on Wed Jul 26 15:04:20 2006\"\n",
      "graph\n",
      "[\n",
      "  directed 0\n",
      "  node\n",
      "  [\n",
      "    id 0\n",
      "    label \"Beak\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 1\n",
      "    label \"Beescratch\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 2\n",
      "    label \"Bumper\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 3\n",
      "    label \"CCL\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 4\n",
      "    label \"Cross\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 5\n",
      "    label \"DN16\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 6\n",
      "    label \"DN21\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 7\n",
      "    label \"DN63\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 8\n",
      "    label \"Double\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 9\n",
      "    label \"Feather\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 10\n",
      "    label \"Fish\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 11\n",
      "    label \"Five\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 12\n",
      "    label \"Fork\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 13\n",
      "    label \"Gallatin\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 14\n",
      "    label \"Grin\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 15\n",
      "    label \"Haecksel\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 16\n",
      "    label \"Hook\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 17\n",
      "    label \"Jet\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 18\n",
      "    label \"Jonah\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 19\n",
      "    label \"Knit\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 20\n",
      "    label \"Kringel\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 21\n",
      "    label \"MN105\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 22\n",
      "    label \"MN23\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 23\n",
      "    label \"MN60\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 24\n",
      "    label \"MN83\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 25\n",
      "    label \"Mus\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 26\n",
      "    label \"Notch\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 27\n",
      "    label \"Number1\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 28\n",
      "    label \"Oscar\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 29\n",
      "    label \"Patchback\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 30\n",
      "    label \"PL\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 31\n",
      "    label \"Quasi\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 32\n",
      "    label \"Ripplefluke\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 33\n",
      "    label \"Scabs\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 34\n",
      "    label \"Shmuddel\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 35\n",
      "    label \"SMN5\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 36\n",
      "    label \"SN100\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 37\n",
      "    label \"SN4\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 38\n",
      "    label \"SN63\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 39\n",
      "    label \"SN89\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 40\n",
      "    label \"SN9\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 41\n",
      "    label \"SN90\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 42\n",
      "    label \"SN96\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 43\n",
      "    label \"Stripes\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 44\n",
      "    label \"Thumper\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 45\n",
      "    label \"Topless\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 46\n",
      "    label \"TR120\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 47\n",
      "    label \"TR77\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 48\n",
      "    label \"TR82\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 49\n",
      "    label \"TR88\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 50\n",
      "    label \"TR99\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 51\n",
      "    label \"Trigger\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 52\n",
      "    label \"TSN103\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 53\n",
      "    label \"TSN83\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 54\n",
      "    label \"Upbang\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 55\n",
      "    label \"Vau\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 56\n",
      "    label \"Wave\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 57\n",
      "    label \"Web\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 58\n",
      "    label \"Whitetip\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 59\n",
      "    label \"Zap\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 60\n",
      "    label \"Zig\"\n",
      "  ]\n",
      "  node\n",
      "  [\n",
      "    id 61\n",
      "    label \"Zipfel\"\n",
      "  ]\n",
      "  edge\n",
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      "    source 8\n",
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      "  edge\n",
      "  [\n",
      "    source 47\n",
      "    target 10\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 47\n",
      "    target 20\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 47\n",
      "    target 28\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 47\n",
      "    target 30\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 47\n",
      "    target 42\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 49\n",
      "    target 34\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 49\n",
      "    target 46\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 14\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 16\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 20\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 33\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 42\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 50\n",
      "    target 45\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 4\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 11\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 18\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 21\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 23\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 24\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 29\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 45\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 51\n",
      "    target 50\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 52\n",
      "    target 14\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 52\n",
      "    target 29\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 52\n",
      "    target 38\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 52\n",
      "    target 40\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 53\n",
      "    target 43\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 1\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 6\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 7\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 13\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 19\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 54\n",
      "    target 41\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 55\n",
      "    target 15\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 55\n",
      "    target 51\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 56\n",
      "    target 5\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 56\n",
      "    target 6\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 5\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 6\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 9\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 13\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 17\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 39\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 41\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 48\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 57\n",
      "    target 54\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 58\n",
      "    target 38\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 59\n",
      "    target 3\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 59\n",
      "    target 8\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 59\n",
      "    target 15\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 59\n",
      "    target 36\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 59\n",
      "    target 45\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 60\n",
      "    target 32\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 61\n",
      "    target 2\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 61\n",
      "    target 37\n",
      "  ]\n",
      "  edge\n",
      "  [\n",
      "    source 61\n",
      "    target 53\n",
      "  ]\n",
      "]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(gml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.parse_gml(gml)\n",
    "colors = ['darkgreen' for n in G]\n",
    "options = {\n",
    "    'node_color':colors,\n",
    "    'node_size':50,\n",
    "    'linewidths': 0,\n",
    "    'width':0.1,\n",
    "    'with_labels':False,\n",
    "}\n",
    "nx.draw_networkx(G,**options)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_central = nx.degree_centrality(G)\n",
    "df = pd.DataFrame.from_dict(dict_central,orient = 'index').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 0], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "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>index</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Beak</td>\n",
       "      <td>0.098361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Beescratch</td>\n",
       "      <td>0.131148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bumper</td>\n",
       "      <td>0.065574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CCL</td>\n",
       "      <td>0.049180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cross</td>\n",
       "      <td>0.016393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        index         0\n",
       "0        Beak  0.098361\n",
       "1  Beescratch  0.131148\n",
       "2      Bumper  0.065574\n",
       "3         CCL  0.049180\n",
       "4       Cross  0.016393"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {'index': 'dophin_type',0:'centrality_degree'},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the weaknest link is Beak, with a centrality_degree of: 0.09836065573770492\n"
     ]
    }
   ],
   "source": [
    "df = df.sort_values('centrality_degree')\n",
    "print(\"the weaknest link is \" + str(df['dophin_type'][0]) + \", with a centrality_degree of: \" + str(df['centrality_degree'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the most effective node\n",
    "df = df.sort_values('centrality_degree',ascending = False)"
   ]
  },
  {
   "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>dophin_type</th>\n",
       "      <th>centrality_degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Grin</td>\n",
       "      <td>0.196721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Topless</td>\n",
       "      <td>0.180328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SN4</td>\n",
       "      <td>0.180328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Scabs</td>\n",
       "      <td>0.163934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Trigger</td>\n",
       "      <td>0.163934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dophin_type  centrality_degree\n",
       "14        Grin           0.196721\n",
       "45     Topless           0.180328\n",
       "37         SN4           0.180328\n",
       "33       Scabs           0.163934\n",
       "51     Trigger           0.163934"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing the node which has the highest centrality degree, the plot is below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G.remove_node('Grin')\n",
    "nx.draw(G)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No much difference since the centrality degree is only about 0.197, let's remove the top 5 nodes with highest centrality degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove the top 5 nodes with highest centrality degrees\n",
    "G.remove_node('Topless')\n",
    "G.remove_node(\"SN4\")\n",
    "G.remove_node('Scabs')\n",
    "G.remove_node('Trigger')\n",
    "nx.draw(G)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the nodes lost their edges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write up\n",
    "I used the dophin network data to see the relationship between them.\n",
    "To see how the network affected by removing the most effected node, the centrality degree is considered as a parameter to evaluate if the importance of the nodes. To finding the node with the highest centrality degree. \"Grin\" has the highest degree of about 0.197. After removing \"Grin\" node, the graph doesn't changed a lot. Due to the low degree, I removed the top 5 nodes with highest degree, the plot shows that some of the nodes lost their edges. In conclusion, removing a node with a significant highest centrality degree, the impact to the network is expected to be affected. "
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}