degree_eigen_hits.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('name_name.csv', dtype={\"reps\": np.int64, \"comm\" : str, \"auth\" : str})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>reps</th>\n",
       "      <th>auth</th>\n",
       "      <th>comm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>Maria Shaldibina</td>\n",
       "      <td>Stev Witzel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>Eduardo Valentin</td>\n",
       "      <td>Daniel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Eric W. Biederman</td>\n",
       "      <td>geiti94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>519</td>\n",
       "      <td>Paul Jakma</td>\n",
       "      <td>Paul Jakma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>122</td>\n",
       "      <td>Laxman Dewangan</td>\n",
       "      <td>Stephen Warren</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   reps               auth            comm\n",
       "0    26   Maria Shaldibina     Stev Witzel\n",
       "1    17   Eduardo Valentin          Daniel\n",
       "2    24  Eric W. Biederman         geiti94\n",
       "3   519         Paul Jakma      Paul Jakma\n",
       "4   122    Laxman Dewangan  Stephen Warren"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "345"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>reps</th>\n",
       "      <th>auth</th>\n",
       "      <th>comm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>root</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5867</th>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12673</th>\n",
       "      <td>18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>舒志凌</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13649</th>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jeff Garzik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18747</th>\n",
       "      <td>21</td>\n",
       "      <td>John Axel Eriksson</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23358</th>\n",
       "      <td>21</td>\n",
       "      <td>Fish</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25165</th>\n",
       "      <td>22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>黄志伟</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27935</th>\n",
       "      <td>477</td>\n",
       "      <td>NaN</td>\n",
       "      <td>git-darcs-import</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29367</th>\n",
       "      <td>18</td>\n",
       "      <td>Lars van de Kerkhof</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29690</th>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30927</th>\n",
       "      <td>491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zorba Build Bot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34674</th>\n",
       "      <td>21</td>\n",
       "      <td>Ralf Baechle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38869</th>\n",
       "      <td>36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>James Bottomley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47195</th>\n",
       "      <td>42</td>\n",
       "      <td>Bharat Mediratta</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47788</th>\n",
       "      <td>17</td>\n",
       "      <td>David Cramer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48084</th>\n",
       "      <td>94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Zsolt Zsuffa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49892</th>\n",
       "      <td>19</td>\n",
       "      <td>Alessandro Roncone</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51842</th>\n",
       "      <td>26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Flavio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53226</th>\n",
       "      <td>507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54115</th>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Macx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61682</th>\n",
       "      <td>17</td>\n",
       "      <td>Tim Almdal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64176</th>\n",
       "      <td>485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CloudCannon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65894</th>\n",
       "      <td>22</td>\n",
       "      <td>Toon Verstraelen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76792</th>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>李宁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77816</th>\n",
       "      <td>74</td>\n",
       "      <td>amazonwebservices</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90434</th>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paul Aurich</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97820</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HenriDellal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103976</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR4T4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108113</th>\n",
       "      <td>1</td>\n",
       "      <td>Deji69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111735</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Luke Schierer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1466001</th>\n",
       "      <td>6</td>\n",
       "      <td>Alexander Gavrilov</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1467211</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>geng</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479381</th>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tobias Rundstrom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1479809</th>\n",
       "      <td>6</td>\n",
       "      <td>gumptionthomas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1486616</th>\n",
       "      <td>6</td>\n",
       "      <td>root</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487108</th>\n",
       "      <td>6</td>\n",
       "      <td>Matt Zweig</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1498227</th>\n",
       "      <td>7</td>\n",
       "      <td>Andy Staudacher</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1499273</th>\n",
       "      <td>7</td>\n",
       "      <td>Jeff Finch</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1508036</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>风往北吹_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1514632</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Murthy Narkedimilli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1515742</th>\n",
       "      <td>7</td>\n",
       "      <td>Andrew Whitworth</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530103</th>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>唐资富</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531532</th>\n",
       "      <td>8</td>\n",
       "      <td>babbaj</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537327</th>\n",
       "      <td>8</td>\n",
       "      <td>Padillla</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1545907</th>\n",
       "      <td>9</td>\n",
       "      <td>Bishakh Ghosh</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547038</th>\n",
       "      <td>9</td>\n",
       "      <td>Mike Christie</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1548045</th>\n",
       "      <td>9</td>\n",
       "      <td>Moore, Eric</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1553713</th>\n",
       "      <td>9</td>\n",
       "      <td>sierra</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1560096</th>\n",
       "      <td>10</td>\n",
       "      <td>andrew.vasquez@qlogic.com</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1589270</th>\n",
       "      <td>12</td>\n",
       "      <td>Andrew Dutcher</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607642</th>\n",
       "      <td>15</td>\n",
       "      <td>Michael Bayne</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1610097</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tarmac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1613815</th>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ChanIan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616698</th>\n",
       "      <td>16</td>\n",
       "      <td>J. David Ibanez</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618849</th>\n",
       "      <td>16</td>\n",
       "      <td>Damien Lebrun</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1642867</th>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GitHub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1706765</th>\n",
       "      <td>79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GitHub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1739879</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GitHub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1894007</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GitHub</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2146176</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GitHub</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         reps                       auth                 comm\n",
       "2295      225                        NaN                 root\n",
       "5867       24                        NaN                 mark\n",
       "12673      18                        NaN                  舒志凌\n",
       "13649      55                        NaN          Jeff Garzik\n",
       "18747      21         John Axel Eriksson                  NaN\n",
       "23358      21                       Fish                  NaN\n",
       "25165      22                        NaN                  黄志伟\n",
       "27935     477                        NaN     git-darcs-import\n",
       "29367      18        Lars van de Kerkhof                  NaN\n",
       "29690      27                        NaN                 Ease\n",
       "30927     491                        NaN      Zorba Build Bot\n",
       "34674      21               Ralf Baechle                  NaN\n",
       "38869      36                        NaN      James Bottomley\n",
       "47195      42           Bharat Mediratta                  NaN\n",
       "47788      17               David Cramer                  NaN\n",
       "48084      94                        NaN         Zsolt Zsuffa\n",
       "49892      19         Alessandro Roncone                  NaN\n",
       "51842      26                        NaN               Flavio\n",
       "53226     507                        NaN                  NaN\n",
       "54115      48                        NaN                 Macx\n",
       "61682      17                 Tim Almdal                  NaN\n",
       "64176     485                        NaN          CloudCannon\n",
       "65894      22           Toon Verstraelen                  NaN\n",
       "76792      21                        NaN                   李宁\n",
       "77816      74          amazonwebservices                  NaN\n",
       "90434      17                        NaN          Paul Aurich\n",
       "97820       1                        NaN          HenriDellal\n",
       "103976      1                        NaN                IR4T4\n",
       "108113      1                     Deji69                  NaN\n",
       "111735      1                        NaN        Luke Schierer\n",
       "...       ...                        ...                  ...\n",
       "1466001     6         Alexander Gavrilov                  NaN\n",
       "1467211     6                        NaN                 geng\n",
       "1479381     6                        NaN     Tobias Rundstrom\n",
       "1479809     6             gumptionthomas                  NaN\n",
       "1486616     6                       root                  NaN\n",
       "1487108     6                 Matt Zweig                  NaN\n",
       "1498227     7            Andy Staudacher                  NaN\n",
       "1499273     7                 Jeff Finch                  NaN\n",
       "1508036     7                        NaN                风往北吹_\n",
       "1514632     7                        NaN  Murthy Narkedimilli\n",
       "1515742     7           Andrew Whitworth                  NaN\n",
       "1530103     8                        NaN                  唐资富\n",
       "1531532     8                     babbaj                  NaN\n",
       "1537327     8                   Padillla                  NaN\n",
       "1545907     9              Bishakh Ghosh                  NaN\n",
       "1547038     9              Mike Christie                  NaN\n",
       "1548045     9                Moore, Eric                  NaN\n",
       "1553713     9                     sierra                  NaN\n",
       "1560096    10  andrew.vasquez@qlogic.com                  NaN\n",
       "1589270    12             Andrew Dutcher                  NaN\n",
       "1607642    15              Michael Bayne                  NaN\n",
       "1610097    15                        NaN               Tarmac\n",
       "1613815    15                        NaN              ChanIan\n",
       "1616698    16            J. David Ibanez                  NaN\n",
       "1618849    16              Damien Lebrun                  NaN\n",
       "1642867    62                        NaN               GitHub\n",
       "1706765    79                        NaN               GitHub\n",
       "1739879     1                        NaN               GitHub\n",
       "1894007     2                        NaN               GitHub\n",
       "2146176     9                        NaN               GitHub\n",
       "\n",
       "[344 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_rows = df[df.isnull().any(1)]\n",
    "nan_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dfn = df.sample(1000)\n",
    "#del df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#G = nx.DiGraph(directed=True)\n",
    "#G.add_edge(1, 3, weight=8)\n",
    "#G.add_edge(3, 1, weight=1)\n",
    "#G.add_edge(3, 4, weight=6)\n",
    "#G.add_edge(1, 2, weight=1)\n",
    "#G.add_edge(2, 4, weight=3)\n",
    "\n",
    "#pos=nx.spring_layout(G) \n",
    "#nx.draw(G,pos)\n",
    "#labels = nx.get_edge_attributes(G,'weight')\n",
    "#nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = nx.from_pandas_edgelist(df, source = 'comm', target = 'auth', edge_attr = 'reps',create_using = nx.DiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del dfn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pos=nx.spring_layout(graph) \n",
    "nx.draw(graph,pos)\n",
    "labels = nx.get_edge_attributes(graph,'weight')\n",
    "nx.draw_networkx_edge_labels(graph,pos,edge_labels=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree centrality\n",
      "Eigenvector centrality\n",
      "HITS\n"
     ]
    }
   ],
   "source": [
    "#print(\"Betweenness\")\n",
    "#b = nx.betweenness_centrality(graph. k = )\n",
    "\n",
    "print(\"Degree centrality\")\n",
    "d = nx.degree_centrality(graph)\n",
    "\n",
    "#print(\"Closeness centrality\")\n",
    "#c = nx.closeness_centrality(graph)\n",
    "\n",
    "print(\"Eigenvector centrality\")\n",
    "pg = nx.pagerank(graph, alpha=0.85, personalization=None, max_iter=100, tol=1e-06, nstart=None, weight='reps', dangling=None)\n",
    "\n",
    "print(\"HITS\")\n",
    "hub, auths = nx.hits(graph, max_iter=100, tol=1e-08, nstart=None, normalized=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('degree_centralities.pickle', 'wb') as handle:\n",
    "    pickle.dump(d, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "with open('page_rank.pickle', 'wb') as handle:\n",
    "    pickle.dump(pg, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "with open('hubs.pickle', 'wb') as handle:\n",
    "    pickle.dump(hub, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "with open('auths.pickle', 'wb') as handle:\n",
    "    pickle.dump(auths, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(\"Diameter\")\n",
    "#largest = max(nx.connected_components(graph.to_undirected()), key=len)\n",
    "#g = graph.to_undirected()\n",
    "#removal = set(g.nodes) - largest\n",
    "#g.remove_nodes_from(removal)\n",
    "#dia = nx.diameter(g)\n",
    "\n",
    "#print(\"Clustering\")\n",
    "#avg_clust = nx.average_clustering(g, weight='reps')\n",
    "#clustering = dict(nx.clustering(g))\n",
    "\n",
    "#del g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top X in each Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 20\n",
    "from heapq import nlargest\n",
    "#sb = nlargest(x, b, key=b.get)\n",
    "#sc = nlargest(x, c, key=c.get)\n",
    "sd = nlargest(x, d, key=d.get)\n",
    "spg = nlargest(x, pg, key=pg.get)\n",
    "shubs = nlargest(x, hub, key=hub.get)\n",
    "sauths = nlargest(x, auths, key=auths.get)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degree ['GitHub', 'Greg Kroah-Hartman', 'Gerrit Code Review', 'Weblate', 'Linus Torvalds', 'David S. Miller', 'Facebook Github Bot', 'Adam Vandenberg', 'root', 'Mike McQuaid', 'Vítor Galvão', 'Prashant Somashekar', 'Pranav Vashi', 'Ben Hutchings', 'jollaman999', 'Steve Kondik', 'Commit bot', 'Chet Kener', 'anarkia1976', 'Josh Chasky']\n",
      "Eigen ['greenkeeper[bot]', 'root', 'unknown', 'Some Popcorn Time Contributor', 'greenkeeperio-bot', 'Greenkeeper', 'dependabot[bot]', 'Tomster', 'conda-forge-admin', 'The Gitter Badger', 'pyup.io bot', 'pyup-bot', 'renovate[bot]', 'vagrant', 'Paul Reioux', 'Your Name', 'Unknown', 'Ubuntu', 'Alex', 'Michael']\n",
      "Hubs ['GitHub', 'Greg Kroah-Hartman', 'Adam Vandenberg', 'Facebook Github Bot', 'Mike McQuaid', 'Weblate', 'Gerrit Code Review', 'Vítor Galvão', 'Sindre Sorhus', 'StyleCI Bot', 'Jack Nagel', 'Dominyk Tiller', 'Linus Torvalds', 'commitay', 'Tim Graham', 'k8s-ci-robot', 'Mohamed Hegazy', 'David S. Miller', 'Andy', 'root']\n",
      "Authorities ['Dan Carpenter', 'Kees Cook', 'Greg Kroah-Hartman', 'Oleg Nesterov', \"Theodore Ts'o\", 'Mel Gorman', 'Stephen Boyd', 'Colin Cross', 'Andi Kleen', 'Dan Williams', 'Guenter Roeck', 'Eric Paris', 'Konstantin Khlebnikov', 'Lars-Peter Clausen', 'Catalin Marinas', 'Lai Jiangshan', 'Paul E. McKenney', 'Paul Moore', 'Andrew Morton', 'Peter Hurley']\n"
     ]
    }
   ],
   "source": [
    "#print(\"Betweenness\", sb)\n",
    "#print(\"Closeness\", sc)\n",
    "print(\"Degree\", sd)\n",
    "print(\"Eigen\", spg)\n",
    "print(\"Hubs\", shubs)\n",
    "print(\"Authorities\", sauths)\n",
    "#print(\"Diameter of largest component\", dia)\n",
    "#print(\"Average clustering coefficient \", avg_clust)\n",
    "#print(\"Clustering coefficient values :\")\n",
    "#print(clustering)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "din = dict(graph.in_degree())\n",
    "dout = dict(graph.out_degree())\n",
    "#for item in sorted(d, key=d.get, reverse=True):\n",
    "#    print (item, d[item])\n",
    "#USE b, c, d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "#din\n",
    "counter_indegree = defaultdict(int)\n",
    "for k, v in din.items():\n",
    "    counter_indegree[v]+=1\n",
    "    \n",
    "#dout\n",
    "counter_outdegree = defaultdict(int)\n",
    "for k, v in dout.items():\n",
    "    counter_outdegree[v]+=1\n",
    "    \n",
    "#b\n",
    "#counter_b = defaultdict(int)\n",
    "#for k, v in b.items():\n",
    "#    counter_b[v]+=1\n",
    "    \n",
    "#c\n",
    "#counter_c = defaultdict(int)\n",
    "#for k, v in c.items():\n",
    "#    counter_c[v]+=1\n",
    "\n",
    "#d\n",
    "counter_d = defaultdict(int)\n",
    "for k, v in d.items():\n",
    "    counter_d[v]+=1\n",
    "    \n",
    "#pg\n",
    "counter_pg = defaultdict(int)\n",
    "for k, v in pg.items():\n",
    "    counter_pg[v]+=1\n",
    "    \n",
    "#hubs and auths\n",
    "\n",
    "counter_hubs = defaultdict(int)\n",
    "for k, v in hub.items():\n",
    "    counter_hubs[v]+=1\n",
    "    \n",
    "counter_auths = defaultdict(int)\n",
    "for k, v in auths.items():\n",
    "    counter_auths[v]+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "             1100: 1,\n",
       "             1193: 1,\n",
       "             344: 4,\n",
       "             779: 1,\n",
       "             94: 23,\n",
       "             242: 5,\n",
       "             501: 1,\n",
       "             478: 2,\n",
       "             128: 12,\n",
       "             227: 7,\n",
       "             1822: 1,\n",
       "             861: 2,\n",
       "             538: 1,\n",
       "             611: 1,\n",
       "             595: 1,\n",
       "             1455: 1,\n",
       "             271: 2,\n",
       "             475: 2,\n",
       "             1045: 1,\n",
       "             793: 1,\n",
       "             389: 1,\n",
       "             285: 2,\n",
       "             1119: 1,\n",
       "             668: 1,\n",
       "             231: 6,\n",
       "             1527: 1,\n",
       "             1344: 1,\n",
       "             352: 1,\n",
       "             250: 8,\n",
       "             409: 2,\n",
       "             657: 1,\n",
       "             683: 2,\n",
       "             312: 5,\n",
       "             1097: 2,\n",
       "             898: 2,\n",
       "             178: 6,\n",
       "             252: 2,\n",
       "             195: 5,\n",
       "             736: 1,\n",
       "             547: 1,\n",
       "             517: 1,\n",
       "             626: 1,\n",
       "             449: 1,\n",
       "             136: 6,\n",
       "             699: 1,\n",
       "             307: 3,\n",
       "             361: 2,\n",
       "             182: 9,\n",
       "             1334: 1,\n",
       "             320: 2,\n",
       "             313: 1,\n",
       "             286: 3,\n",
       "             1163: 1,\n",
       "             496: 2,\n",
       "             838: 1,\n",
       "             347: 3,\n",
       "             790: 2,\n",
       "             201: 5,\n",
       "             245: 2,\n",
       "             607: 1,\n",
       "             152: 7,\n",
       "             168: 8,\n",
       "             155: 11,\n",
       "             224: 9,\n",
       "             1502: 1,\n",
       "             427: 1,\n",
       "             381: 1,\n",
       "             102: 26,\n",
       "             284: 2,\n",
       "             591: 1,\n",
       "             1007: 1,\n",
       "             894: 1,\n",
       "             764: 1,\n",
       "             334: 2,\n",
       "             474: 2,\n",
       "             1843: 1,\n",
       "             316: 1,\n",
       "             498: 3,\n",
       "             1165: 2,\n",
       "             457: 1,\n",
       "             331: 4,\n",
       "             904: 1,\n",
       "             184: 11,\n",
       "             592: 2,\n",
       "             426: 2,\n",
       "             612: 1,\n",
       "             652: 1,\n",
       "             1612: 1,\n",
       "             1413: 1,\n",
       "             1331: 1,\n",
       "             434: 1,\n",
       "             725: 1,\n",
       "             1015: 1,\n",
       "             703: 1,\n",
       "             619: 1,\n",
       "             486: 2,\n",
       "             189: 9,\n",
       "             1065: 1,\n",
       "             306: 4,\n",
       "             167: 10,\n",
       "             534: 1,\n",
       "             309: 1,\n",
       "             1323: 1,\n",
       "             456: 1,\n",
       "             366: 2,\n",
       "             1436: 1,\n",
       "             471: 2,\n",
       "             142: 17,\n",
       "             444: 1,\n",
       "             733: 1,\n",
       "             1337: 1,\n",
       "             974: 1,\n",
       "             1679: 1,\n",
       "             500: 1,\n",
       "             171: 6,\n",
       "             299: 3,\n",
       "             1140: 1,\n",
       "             520: 2,\n",
       "             211: 7,\n",
       "             363: 3,\n",
       "             70: 53,\n",
       "             188: 6,\n",
       "             1595: 1,\n",
       "             1104: 1,\n",
       "             745: 1,\n",
       "             739: 1,\n",
       "             1184: 1,\n",
       "             290: 4,\n",
       "             908: 1,\n",
       "             332: 1,\n",
       "             370: 2,\n",
       "             512: 2,\n",
       "             425: 2,\n",
       "             1242: 1,\n",
       "             383: 1,\n",
       "             507: 1,\n",
       "             175: 6,\n",
       "             113: 24,\n",
       "             238: 4,\n",
       "             329: 5,\n",
       "             577: 1,\n",
       "             390: 2,\n",
       "             1117: 1,\n",
       "             371: 3,\n",
       "             1625: 1,\n",
       "             1048: 1,\n",
       "             539: 1,\n",
       "             724: 1,\n",
       "             837: 1,\n",
       "             256: 6,\n",
       "             241: 4,\n",
       "             164: 7,\n",
       "             740: 1,\n",
       "             522: 1,\n",
       "             601: 2,\n",
       "             742: 1,\n",
       "             104: 22,\n",
       "             403: 2,\n",
       "             633: 1,\n",
       "             229: 4,\n",
       "             298: 3,\n",
       "             525: 2,\n",
       "             429: 1,\n",
       "             249: 3,\n",
       "             365: 1,\n",
       "             356: 2,\n",
       "             721: 1,\n",
       "             1570: 1,\n",
       "             1316: 1,\n",
       "             549: 2,\n",
       "             304: 2,\n",
       "             247: 2,\n",
       "             462: 1,\n",
       "             953: 1,\n",
       "             796: 1,\n",
       "             667: 1,\n",
       "             119: 19,\n",
       "             620: 1,\n",
       "             199: 4,\n",
       "             303: 3,\n",
       "             481: 2,\n",
       "             441: 2,\n",
       "             666: 1,\n",
       "             397: 1,\n",
       "             232: 3,\n",
       "             153: 9,\n",
       "             351: 5,\n",
       "             275: 3,\n",
       "             198: 4,\n",
       "             310: 2,\n",
       "             394: 1,\n",
       "             214: 6,\n",
       "             454: 2,\n",
       "             442: 1,\n",
       "             341: 1,\n",
       "             305: 3,\n",
       "             672: 1,\n",
       "             219: 6,\n",
       "             193: 5,\n",
       "             470: 1,\n",
       "             515: 1,\n",
       "             536: 2,\n",
       "             220: 5,\n",
       "             133: 12,\n",
       "             802: 1,\n",
       "             398: 1,\n",
       "             771: 1,\n",
       "             439: 1,\n",
       "             437: 1,\n",
       "             890: 1,\n",
       "             1086: 1,\n",
       "             258: 6,\n",
       "             690: 1,\n",
       "             694: 1,\n",
       "             1014: 2,\n",
       "             1028: 1,\n",
       "             302: 2,\n",
       "             157: 6,\n",
       "             1161: 1,\n",
       "             254: 2,\n",
       "             637: 1,\n",
       "             786: 1,\n",
       "             585: 1,\n",
       "             405: 1,\n",
       "             523: 1,\n",
       "             236: 1,\n",
       "             257: 2,\n",
       "             308: 1,\n",
       "             599: 1,\n",
       "             606: 1,\n",
       "             233: 4,\n",
       "             1053: 1,\n",
       "             762: 1,\n",
       "             407: 1,\n",
       "             333: 2,\n",
       "             300: 2,\n",
       "             273: 4,\n",
       "             410: 1,\n",
       "             982: 1,\n",
       "             750: 1,\n",
       "             563: 1,\n",
       "             414: 1,\n",
       "             589: 1,\n",
       "             464: 1,\n",
       "             358: 1,\n",
       "             447: 1,\n",
       "             502: 1,\n",
       "             477: 2,\n",
       "             548: 1,\n",
       "             810: 1,\n",
       "             415: 2,\n",
       "             615: 1,\n",
       "             355: 1,\n",
       "             553: 1,\n",
       "             1057: 1,\n",
       "             519: 1,\n",
       "             813: 1,\n",
       "             335: 3,\n",
       "             847: 1,\n",
       "             311: 1,\n",
       "             1062: 1,\n",
       "             325: 3,\n",
       "             386: 1,\n",
       "             818: 1,\n",
       "             728: 1,\n",
       "             526: 1,\n",
       "             857: 1,\n",
       "             479: 1,\n",
       "             748: 1,\n",
       "             359: 1,\n",
       "             192: 3,\n",
       "             628: 1,\n",
       "             564: 1,\n",
       "             268: 3,\n",
       "             301: 1,\n",
       "             504: 1,\n",
       "             921: 1,\n",
       "             315: 1,\n",
       "             455: 1,\n",
       "             655: 1,\n",
       "             264: 1,\n",
       "             622: 1,\n",
       "             293: 1,\n",
       "             253: 1,\n",
       "             353: 1,\n",
       "             588: 1,\n",
       "             230: 2,\n",
       "             480: 1,\n",
       "             385: 1,\n",
       "             1178: 1,\n",
       "             267: 2,\n",
       "             318: 1,\n",
       "             1217: 1,\n",
       "             294: 1,\n",
       "             609: 1,\n",
       "             283: 1,\n",
       "             532: 1,\n",
       "             533: 1,\n",
       "             388: 1,\n",
       "             588236: 1})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter_outdegree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del counter_outdegree[283]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.bar(counter_indegree.keys(), counter_indegree.values(), 0.05)\n",
    "lists = sorted(counter_indegree.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlim([0, 10])\n",
    "plt.plot(x, y)\n",
    "plt.savefig('dist_in_degree.png', bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.bar(counter_outdegree.keys(), counter_outdegree.values(), 0.5)\n",
    "lists = sorted(counter_outdegree.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlim([0, 10])\n",
    "plt.plot(x, y)\n",
    "plt.savefig('dist_out_degree.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Undirected Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## HITS, SAVE IMAGES\n",
    "G2 = graph.to_undirected()\n",
    "ddegree = dict(G2.degree())\n",
    "counter_ddegree = defaultdict(int)\n",
    "for k, v in ddegree.items():\n",
    "    counter_ddegree[v]+=1\n",
    "\n",
    "lists = sorted(counter_ddegree.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlim([0, 10])\n",
    "plt.plot(x, y)\n",
    "plt.savefig('dist_und_degree.png', bbox_inches='tight')\n",
    "plt.show()\n",
    "\n",
    "del G2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counter_ddegree == counter_outdegree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Betweeness Centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#plt.bar(counter_b.keys(), counter_b.values(), 0.05)\n",
    "lists = sorted(counter_b.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(x, y)\n",
    "plt.savefig('dist_centrality_betweeness.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Degree Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.bar(counter_d.keys(), counter_d.values(), 0.05)\n",
    "lists = sorted(counter_d.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlim([0, 0.002])\n",
    "plt.plot(x, np.log10(y))\n",
    "plt.savefig('dist_centrality_degree.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Closeness Centrality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#plt.bar(counter_c.keys(), counter_c.values(), 0.05)\n",
    "lists = sorted(counter_c.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.savefig('dist_centrality_closeness.png', bbox_inches='tight')\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Eigen-vector (Page Rank) Centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lists = sorted(counter_pg.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlim([0.0000005, 0.000004])\n",
    "plt.plot(x, np.log10(y))\n",
    "plt.savefig('dist_centrality_pagerank.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hubs and Authorities values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sachando/Library/Python/3.6/lib/python/site-packages/ipykernel_launcher.py:5: RuntimeWarning: divide by zero encountered in log10\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lists = sorted(counter_hubs.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "#plt.xlim([0, 1])\n",
    "plt.plot(np.log10(x), np.log10(y))\n",
    "plt.savefig('dist_centrality_hubs.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lists = sorted(counter_auths.items()) # sorted by key, return a list of tuples\n",
    "x, y = zip(*lists) # unpack a list of pairs into two tuples\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(x, y)\n",
    "plt.savefig('dist_centrality_auths.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(int,\n",
       "            {1.7006392575098756e-06: 1,\n",
       "             1.7043484001413592e-06: 1,\n",
       "             1.758043382585282e-06: 1,\n",
       "             2.0259115302336317e-08: 1,\n",
       "             3.316637799478166e-10: 1,\n",
       "             2.0942982600086508e-07: 1,\n",
       "             1.4385364061652826e-08: 1,\n",
       "             1.3046073833392852e-07: 1,\n",
       "             9.787201251229377e-08: 1,\n",
       "             2.7414003191733902e-08: 1,\n",
       "             2.3646161521821138e-07: 1,\n",
       "             5.274257047084022e-11: 1,\n",
       "             1.5537602334169615e-09: 1,\n",
       "             4.1986154764348344e-09: 1419,\n",
       "             1.7860638268286665e-06: 1,\n",
       "             1.6997609348218206e-06: 1,\n",
       "             1.7207566359896452e-06: 1,\n",
       "             5.652167648433948e-10: 1,\n",
       "             9.880146200019299e-11: 1,\n",
       "             9.781660805372848e-09: 1,\n",
       "             1.0564450929390988e-10: 3,\n",
       "             6.746819390364079e-10: 1,\n",
       "             4.0572378063455866e-11: 6,\n",
       "             8.660487380202383e-09: 1,\n",
       "             4.715275851149435e-09: 1,\n",
       "             2.2862656893720565e-93: 29842,\n",
       "             1.6995594633436326e-06: 1,\n",
       "             2.517997774574119e-10: 1,\n",
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