11_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "description: Various real-life large graphs and their characteristics used in [Related\n",
    "  works](02_related.ipynb)\n",
    "output-file: datasets.html\n",
    "title: Graph datasets\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp graph_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| output: false\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imports SciPy modules and matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Datasets from FELINE (`table_fel`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Reachability Queries in Very Large Graphs: A Fast Refined Online Search Approach\" (2014)  \n",
    "http://openprocedings.org/EDBT/2014/paper_166.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Table 1: Datasets](images/feline/FELINE_paper-table_1-datasets-with_caption.png)\n",
    "\n",
    "- _vertices_ - $|V|$, number of vertices / nodes in the graph\n",
    "- _edges_ - $|E|$, number of edges in the graph\n",
    "- _Cluster-coeff_ - clustering coefficient\n",
    "- _Eff-diameter_ - effective diameter (or effective eccentricity) is an estimated size of the path in which 90% of all pairs of vertices connected are reachable from each other\n",
    "- _roots_ - number of roots, nodes with no incoming edges, vertices with no predecessors (sources)\n",
    "- _leafs_ - number of leafs, nodes with no outgoing edges (sinks)\n",
    "\n",
    "The FELINE paper authors used the SNAP software <snap.stanford.edu/snap/> to compute these values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\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>vertices</th>\n",
       "      <th>edges</th>\n",
       "      <th>edges[% V^2]</th>\n",
       "      <th>edges/vertices</th>\n",
       "      <th>Cluster coeff.</th>\n",
       "      <th>Eff. diameter</th>\n",
       "      <th>roots</th>\n",
       "      <th>leafs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graph</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arXiv</th>\n",
       "      <td>6000</td>\n",
       "      <td>66707</td>\n",
       "      <td>0.185297</td>\n",
       "      <td>11.117833</td>\n",
       "      <td>0.35</td>\n",
       "      <td>5.48</td>\n",
       "      <td>961</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yago</th>\n",
       "      <td>6642</td>\n",
       "      <td>42392</td>\n",
       "      <td>0.096092</td>\n",
       "      <td>6.382415</td>\n",
       "      <td>0.24</td>\n",
       "      <td>6.57</td>\n",
       "      <td>5176</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go</th>\n",
       "      <td>6793</td>\n",
       "      <td>13361</td>\n",
       "      <td>0.028954</td>\n",
       "      <td>1.966878</td>\n",
       "      <td>0.07</td>\n",
       "      <td>10.92</td>\n",
       "      <td>64</td>\n",
       "      <td>3087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed</th>\n",
       "      <td>9000</td>\n",
       "      <td>40028</td>\n",
       "      <td>0.049417</td>\n",
       "      <td>4.447556</td>\n",
       "      <td>0.10</td>\n",
       "      <td>6.32</td>\n",
       "      <td>2609</td>\n",
       "      <td>4702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseer</th>\n",
       "      <td>10720</td>\n",
       "      <td>44258</td>\n",
       "      <td>0.038513</td>\n",
       "      <td>4.128545</td>\n",
       "      <td>0.28</td>\n",
       "      <td>8.36</td>\n",
       "      <td>4572</td>\n",
       "      <td>1868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot22m</th>\n",
       "      <td>1595444</td>\n",
       "      <td>1595442</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1556157</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cit-patents</th>\n",
       "      <td>3774768</td>\n",
       "      <td>16518047</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>4.375911</td>\n",
       "      <td>0.09</td>\n",
       "      <td>10.50</td>\n",
       "      <td>515785</td>\n",
       "      <td>1685423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseerx</th>\n",
       "      <td>6540401</td>\n",
       "      <td>15011260</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>2.295159</td>\n",
       "      <td>0.06</td>\n",
       "      <td>8.40</td>\n",
       "      <td>567149</td>\n",
       "      <td>5740710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go-uniprot</th>\n",
       "      <td>6967956</td>\n",
       "      <td>34770235</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>4.990019</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.80</td>\n",
       "      <td>6945721</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot100m</th>\n",
       "      <td>16087295</td>\n",
       "      <td>16087293</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.10</td>\n",
       "      <td>14598959</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot150m</th>\n",
       "      <td>25037600</td>\n",
       "      <td>25037598</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4.40</td>\n",
       "      <td>21650056</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             vertices     edges  edges[% V^2]  edges/vertices  Cluster coeff.  \\\n",
       "Graph                                                                           \n",
       "arXiv            6000     66707      0.185297       11.117833            0.35   \n",
       "Yago             6642     42392      0.096092        6.382415            0.24   \n",
       "Go               6793     13361      0.028954        1.966878            0.07   \n",
       "Pubmed           9000     40028      0.049417        4.447556            0.10   \n",
       "citeseer        10720     44258      0.038513        4.128545            0.28   \n",
       "Uniprot22m    1595444   1595442      0.000063        0.999999            0.00   \n",
       "Cit-patents   3774768  16518047      0.000116        4.375911            0.09   \n",
       "citeseerx     6540401  15011260      0.000035        2.295159            0.06   \n",
       "Go-uniprot    6967956  34770235      0.000072        4.990019            0.00   \n",
       "Uniprot100m  16087295  16087293      0.000006        1.000000            0.00   \n",
       "Uniprot150m  25037600  25037598      0.000004        1.000000            0.00   \n",
       "\n",
       "             Eff. diameter     roots    leafs  \n",
       "Graph                                          \n",
       "arXiv                 5.48       961      624  \n",
       "Yago                  6.57      5176      263  \n",
       "Go                   10.92        64     3087  \n",
       "Pubmed                6.32      2609     4702  \n",
       "citeseer              8.36      4572     1868  \n",
       "Uniprot22m            3.30   1556157        1  \n",
       "Cit-patents          10.50    515785  1685423  \n",
       "citeseerx             8.40    567149  5740710  \n",
       "Go-uniprot            4.80   6945721        4  \n",
       "Uniprot100m           4.10  14598959        1  \n",
       "Uniprot150m           4.40  21650056        1  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Table 1: Datasets from FELINE (table_fel) { form-width: \"25%\" }\n",
    "\n",
    "csv=u\"\"\"Graph;vertices;edges;Cluster coeff.;Eff. diameter;roots;leafs\n",
    "arXiv;6000;66707;0.35;5.48;961;624\n",
    "Yago;6642;42392;0.24;6.57;5176;263\n",
    "Go;6793;13361;0.07;10.92;64;3087\n",
    "Pubmed;9000;40028;0.10;6.32;2609;4702\n",
    "citeseer;10720;44258;0.28;8.36;4572;1868\n",
    "Uniprot22m;1595444;1595442;0.00;3.3;1556157;1\n",
    "Cit-patents;3774768;16518047;0.09;10.5;515785;1685423\n",
    "citeseerx;6540401;15011260;0.06;8.4;567149;5740710\n",
    "Go-uniprot;6967956;34770235;0.00;4.8;6945721;4\n",
    "Uniprot100m;16087295;16087293;0.00;4.1;14598959;1\n",
    "Uniprot150m;25037600;25037598;0.00;4.4;21650056;1\n",
    "\"\"\"\n",
    "csv_stream = io.StringIO(csv)\n",
    "\n",
    "table_fel=pd.read_csv(csv_stream,sep=';',index_col=0)\n",
    "table_fel.insert(loc=2,column='edges[% V^2]',\n",
    "               value=100.0*(table_fel['edges']/(table_fel['vertices']**2)))\n",
    "table_fel.insert(loc=3,column='edges/vertices',\n",
    "               value=table_fel['edges']/table_fel['vertices'])\n",
    "table_fel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee=np.geomspace(5000,25000000,num=10)\n",
    "ax=table_fel[table_fel['vertices'] < 100000].plot.scatter(x='vertices',y='edges',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,label='small',\n",
    "                          title='Graphs from FELINE paper, Table 1')\n",
    "ax=table_fel[table_fel['vertices'] >= 100000].plot.scatter(ax=ax,x='vertices',y='edges',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,c='red',label='large',\n",
    "                          title='Graphs from FELINE paper, Table 1')\n",
    "ax.plot(ee,2*ee,'-',label='dense vs sparse',c='gray')\n",
    "for row in table_fel.itertuples():\n",
    "    ax.annotate(row[0], xy=(row.vertices,row.edges),\n",
    "                xytext=(3,-3), textcoords='offset points',horizontalalignment='left',\n",
    "                family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "ax.set_xlim(left  =2e3)\n",
    "ax.set_ylim(bottom=5e3)\n",
    "plt.legend(loc='upper left')\n",
    "#plt.savefig('feline_graphs_vertices_vs_edges.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bTVRUlHniiSfMt99+a4wxJioqyiQlJRljjJk9e7YZOnSoe/tGjRqZEydOmOzsbPPAAw+Ytm3bmnbt2pmgoCBz6NAhY4wx1atXN8YYs2fPHtOmTRuvx/XWds4x9pEtzxT08pFSyipJJzII8PMjE4e7LMDPj6QTGWW6jNSiRQtWrlzJqlWrGDNmDNe4LgMGBjrr9PPzc/+cv5ybm8u8efNISUlh/fr1BAQEEB0dbelDeLacZMcYs8QYM6xmzZq+DkUpdYGJrBVMjsNRoCzH4SCyVnCZ6j148CDVqlXjjjvu4PHHH2fDhg0l2u/UqVPUr1+fgIAAVqxYwd69e8sUR3FsmRSUUsoqdUICmX7zZQQF+BEaWIWgAD+m33xZmTubN2/eTPfu3YmJiWHKlCmMd93dVZxBgwaxbt06YmNjmTdvHq1atSpTHMWx5eUjpZSyUt+YCLo2q1uudx9de+21XHnllQWGuUj0eAp98ODBDB482Ou6NWvWeK0zLS0NgOjo6HJ5RgFsmhS0T0EpZbU6IYHlkgzsxpaXj7RPQSmlrGHLpKCUUsoamhSUUkq52bJPQakLze+Jicx4910ys7LIyc2l15VX8uuuXUx79FEOHD7Mjr17ievUqUzHOJ2Wxsq1a4nv3t26fSvRHArq/NjyTEHnU1AXkrQzZxj3n/8w+p57mD1lCu888wwXN27MtEcfBeDgkSOs/OmnMh8nNT2dz1assG7fSjaHgjo/tjxTMDr2kbqAfLt2LX/p2JFGrpFGRYQrL7+c+OHDWfL66/x38WK27tzJkHHjeOyuu2jtcddd/hwH2dnZHD52jFFDh9K+TRveW7SIb9etIz0jg6s6dOD+gQP57+LF/LprF0PGjePOG2+kRVQUk19/nazsbAKrVmXSgw9Su2ZN4ocPp89f/8r6LVuoExbG9JEjz9p378GDfLVqFUGBgXTv3JlBXbpUyjkUKpOQkBAOHTrk6zCKZcukoNSF5PCxYzSoW7fI9Xf07cvSlSuZOGKE1/VnMjKYOXEiBw4fZuT06bz//PP0v/Za/t6vHw6Hg8FjxnBjz57c0bcvu/fv541JkwAY9e9/M+zWW7msZUtW/Pgjcz/5hEfvuovcvDx6dO7M/QMHMnziRHbu3XvWvrc/9hhvTp5M9eBg59wM69frHArlKH8coqKGR7eSJgWlfCy8Th127ttXom3PZGTwz8mTOXnyJDXr1wdwD1kdER5O6pkzAHy9Zg0Lly1DRDhw+DDJR49S3zU8dr4de/cy4913AchzOGjUoAEAVfz9adW0KQAN69XjZGoqwUFBBfYdOWQIz775Jnl5efS/9louvxDnULCof6SoobATExPp06cP3bt3Z82aNXz66ae8++67zJs3j0aNGlG3bl06dOjA448/zq5du3jggQdISUmhWrVqvPnmm+X2pLMmBaV87C8dO/L2J59wY8+e7ktIa37+2b0+ICDAPbR1teBgZk+ZQkJCApe3bs2+Q4f4ddcuAA6lpBDiGrp65vvvs/CVV6gaEMDgMWPAmAL1AFzcqBFD+vd3J4CcnByv8Rkv+7a6+GImtW7N4aNHeXjqVOa/8MIFMYeC2/z5zrZUrepMdrNnO4cDLwdFDYUNsH37dt5++21mzpzJunXr+Pjjj9m4caNzCtT27enQoQMAw4YN4/XXX6d58+b8+OOP3H///XzzzTflEl+lSQoi4gc8DdTAOazrOz4OSakKEVKtGpMffphps2aRlZ3tvvsoX/OoKJKSk3n82We5d8AAmhf69h0cGMiIp58m5fhxHr/7bgB6dOnC4DFjiI6IcH/LrxsWRlBgII898wy39unDY3fdxbRZszjjGnHzhquv5rq4OK8xFt73gy++4GRqKlnZ2Qz429+cG9l8DgU3i+eYNsYwduxYvv32W/z8/Dhw4ACHDx8GICoqii5dugDw3Xff0a9fP4JdiT7eNX9GWloa33//Pbfccou7zqysrDLHlc/SpCAic4DrgSPGmEs9ynsDMwB/4C1jzDNAPyACOA4kWRmXUpVNyyZNmOmaPjPf312za1UPDubtadPOue89t95aoGzkkCFet311woQCy4UnzwFY8vrr7p89+zE89+3crp33YOrVs28yyGfxHNPnGgq7evXq7u2Mx8x6nhwOB2FhYfzscTZZnqzuxZgL9PYsEBF/4FWgD9AaGCgirYGWwBpjzKPAfRbHpZRS3lncP1LSobCvuuoqlixZQmZmJmlpaXz++ecA1KhRgyZNmvDhhx8CzuSxadOmcokNLD5TMMZ8KyLRhYo7ATuNMbsBRGQBzrOE/UD+J5GHUqpY/a6+2tchXHgsnmN60KBBxMfHExsbS0xMTJEdxB07dqRv3760a9eOqKgoYmNjyR/vbd68edx3331MnjyZnJwcbrvtNtoVdfZWSlLUKUp5cSWFz/IvH4lIf6C3MWaoa/nvQGfgCeBl4AzwmzHm1SLqGwYMAwgPD++wYMECS+MH5zW8kJAQy4/jC9o2e9K2lU7NmjUp7ajKcvQosncvJioKc45bhksjLy8Pf3//Em+f/16cOXOGPn36MGPGDGJiYkp1zJ07d1L4Qd/u3buvN8bEetveFx3N3mabNsaYM4D3C6EFN5wlIoeA+NDQ0A5xRXSMlaeEhAQq4ji+oG2zJ21b6Wzbtq3APAYlEhoKTZqUaxypqamliuPee+/l119/JTMzkzvvvJNu3bqV+phBQUFcfvnlJd7eF0khCWjksRwJHPRBHEopVam9//77FX5MX4x9tBZoLiJNRKQqcBuwuDQV6HwKSillDUuTgojMB9YALUUkSUSGGGNygRHAV8A24ANjzNZS1qsD4imlSsXq/tPK6HzabPXdR14fATTGLAWWlqFeHRBPKVViQUFBHDt2jDp16iDirVvzwmOM4dixYwQVGqKkOJXmiebS0DmalVKlERkZSVJSEikpKT6NIzMzs9R/pMsiKCiIyMjIUu1jy6SgZwpKqdIICAigSTnfSXQ+EhISSnUnkC/oJDtKKaXcbJkU9O4jpZSyhi2TglJKKWvYMino5SOllLKGLZOCXj5SSilr2DIpKKWUsoYmBaWUUm62TArap6CUUtawZVLQPgWllLKGLZOCUkopa2hSUEop5aZJQSmllJstk4J2NCullDVsmRS0o1kppaxhy6SglFLKGpoUlFJKuWlSUEop5VZpkoKIxInIKhF5XUTifB2PUkr9GVmaFERkjogcEZEthcp7i8h2EdkpIqNdxQZIA4KAJCvjUkop5Z3VZwpzgd6eBSLiD7wK9AFaAwNFpDWwyhjTBxgF/MviuJRSSnlhaVIwxnwLHC9U3AnYaYzZbYzJBhYA/YwxDtf6E0CglXEppZTyTowx1h5AJBr4zBhzqWu5P9DbGDPUtfx3oDPwDXAtEAa8ZoxJKKK+YcAwgPDw8A4LFiywNH6AtLQ0QkJCLD+OL2jb7EnbZk+VpW3du3dfb4yJ9bauSkUHA4iXMmOM+QT4pLidjTGzROQQEB8aGtohLi6uvOM7S0JCAhVxHF/QttmTts2e7NA2X9x9lAQ08liOBA6WpgJ9olkppazhi6SwFmguIk1EpCpwG7C4NBXo2EdKKWUNq29JnQ+sAVqKSJKIDDHG5AIjgK+AbcAHxpitVsahlFKqZCztUzDGDCyifCmwtAz1LgGWxMbG3nO+dSillDpbpXmiuTT08pFSSlnDlklBO5qVUsoatkwKSimlrGHLpKCXj5RSyhq2TAp6+Ugppaxhy6SglFLKGrZMCnr5SCmlrGHLpKCXj5RSyhq2TApKKaWsYcukoJePlFLKGrZMCnr5SCmlrGHLpKCUUsoamhSUUkq5aVJQSinlZsukoB3NSillDVsmBe1oVkopa9gyKSillLKGJgWllFJumhSUUkq5aVJQSinlVqmSgohUF5H1InK9r2NRSqk/I0uTgojMEZEjIrKlUHlvEdkuIjtFZLTHqlHAB1bGpJRSqmhWnynMBXp7FoiIP/Aq0AdoDQwUkdYi0hP4FThscUxKKaWKIMYYaw8gEg18Zoy51LV8BfCUMeZa1/IY16YhQHWciSIDuNEY4/BS3zBgGEB4eHiHBQsWWBo/QFpaGiEhIZYfxxe0bfakbbOnytK27t27rzfGxHpbV6WigwEigP0ey0lAZ2PMCAARGQwc9ZYQAIwxs0TkEBAfGhraIS4uzuJwISEhgYo4ji9o2+xJ22ZPdmibLzqaxUuZ+3TFGDPXGPPZuSrQJ5qVUsoavkgKSUAjj+VI4GBpKtCxj5RSyhq+SAprgeYi0kREqgK3AYtLU4GeKSillDWsviV1PrAGaCkiSSIyxBiTC4wAvgK2AR8YY7aWsl49U1BKKQtYmhSMMQONMQ2NMQHGmEhjzGxX+VJjTAtjzMXGmCnnUa+eKbgcOHyYboMGMWTcOG5/7DGWrlxZ5Hb3TphgWRzxw4dbVrdSquL44u6jMhOReCC+WbNmvg6lUmh98cW8MWkSaWfOcMtDD3HNVVdRxd/fpzEdS8si6UQGkbWCqRMS6NNYlFIlZ8ukYIxZAiyJjY29x9exVCYh1apRt1Yt+t53H0tnzQLg3gkTmPDAAwCknjnDmBdeYO+BA1wXF8eg+Hh+3LqVz3/6iTyHg8SkJB67+24++OIL9icnM2roUDq3a8eOxET+PWcODmMICw3l6YceIigwkBfmzuXnbdtoER1Nbl6eO45FPx9g1Me/EODnR47DwfSbL6NvTIRP3hOlVOmUKCmIyMVAkjEmS0TigMuAd40xJ60L7Zzx6JmCF4ePHePEqVOIeLvrFw4dOcKbTz9N1YAABj3+OH26dQMgz+HghdGj+XLVKl7+73+Z99xz7Ni7l9cXLKBzu3ZMmzWLKY88QsN69Zi3ZAmffv01MZdcws69e3n32Wc5cPgwn379NeA8Qxj18S9k5jjIxPmoyRMf/0LXZnX1jEEpGyjpmcLHQKyINANm47xb6H3gb1YFdi56plDQr7t2MXT8eESEJ++/n8mvveZe5/nEenREBNWDgwFoFhXFgSNHAGjVpAkA4XXq0CwqCn9/f8Lr1uVUaioAu/bvZ/yLLwKQnZND53bt2HvwIG1cSTkiPJzaYWEAJJ3IIMDPz50QAAL8/Eg6kaFJQSkbKGlScBhjckXkRuBFY8zLIrLRysBUyeX3KeRzGEN2Tg55Dgd7kpLc5YkHDnAmI4OqVauyc+9eIurXByhwZuF5jpGfTpo1bswzjz1Gvdq1AcjJyWHnvn0s/uYbAA6lpHD85EkAImsFk+Mo+DB6jsNBZK3gcmqtUspKJU0KOSIyELgTiHeVBVgTUvE8Lx9t37OHl957j8ysLPIcDppERjL23nsJqFJ00y70TtDb/vY3/v7EE7Rs0oT6deu6yxvWr8+kmTPZd/Ag8T16uL/dF2fMsGFMeOkld7/B3TffzBUxMTSNjOQfo0ZxcePG7oRRJySQ6TdfxhOF+hQuxPdZqQuSMabYF85B6l4CBrqWmwCjS7Kvla/2sbGm/4MPmn0HD5p8G7ZuNZlZWaYon25MMi3HLzWXTvjStBy/1CzamFTktvlWrFhR7DZ2ZVXbjqZmmp/3nTBHUzMtqb8k9HOzJ22b9YB1poi/q+c8UxCR5caYq4E7jTEPeiSSPcAzViWqkjJBQfy1UycaNWzoLru8dWuMMTw9cya79u/H4XAwcsgQ2rZooZ2gFahOSKC+p0rZUHGXjxqKyF+BviKygEKD2RljNlgW2TnkXz66pEsXGrgujxw/dYqR06dzMjWVvj16kJuXx9xp00hKTmbU888z77nntBNUKaWKUdwTzROA0TgHrXsBeN7j9W9rQyuacT3RHFSlCslHjwJQu2ZNZk+ZQuuLLyYjM5N2rVoBENmgAafT0pw/ayeoUkqd0zmTgjHmI2NMH2C6MaZ7oVePCoqxSJKZycqffiIpOdldludw4O/nx6bffgMgKTmZ0OrVgT86QYMC/AgNrEJQgJ92giqllIcS3X1kjHlaRGoBzYEgj/JvrQqsJMQYnn7oIaa+8QZZ2dkEVq1Kg7p1uaV3b2a89x6Dx4zB4XAw+p4/HmfoGxNB12Z1L+i7j0rr6IkTvLNwIY/dfTdrN2+mZmgoLaKjKzyOC/2uMKXsoKRPNA8FHsJ5GelnoAvO0U99frbQqmlTZk6ceFb5RNfQDt5oJ2hBdWvV4rG77wZg3ZYtNG7YsMKSQl5eHv7+/jo0hlKVREmfU3gI6Aj8YIzpLiKtgH9ZF5ay2ox332X91q2cSU/naFYWy1avZvrIkSz+5huCAgP5ZNkyZk2axPIffmD+Z59hgCtiYrh3wADWbt7Mi+++S3BgIBfVr8+kBx8scnyk+Z99xv+tXk2ew8ENPXtyU69eLFq+nFXr15Obm0uHNm3429W99a4wpSqJkiaFTGNMpoggIoHGmN9EpKWlkZ2Djn1UNqvWreNQSgrvPPMMK1eu5OKWLVm2ejU1Q0Pp26MHjRs25Lq4OE6npfHeokXMmTqVgCpVeGTaNHYkJvLNDz/wwO23c+Xll+Nwddx7Gx+p02WXsXrjRuZMnYrD4eCusWPp0bkzAGcyMnh1wgREhE37T+pdYUpVEiVNCkkiEgZ8CiwTkROUcgrN8mR07KMy2blvHx3btnUPb+Hn5/1+g32HDnEoJYXhrstzqenpHEpJ4c4bbuDthQtZsmIFndq25cZevbyOj7Rz3z5279/P0PHjAUjPyHDfLXZZy5bu4+tdYUpVHiXtaL7R9eNTIrICqAl8aVlUylLNGjfmi1WruPmaa4CCg+YFVKniHs4iMjycRg0a8MakSVTx98fhcGCMITs3lzHDhmGMoe/999Ora1ev4yPtPXiQVk2b8vyoUYgIObm5BFSpwvY9e/D3SEQ6NIZSlUeJ51MQkauA5saYt0WkHhAB7LEsMmWZbrGxrNuyhX+MGkVGejoD4uPd67rExPDc7NmsWreO6SNHMig+nmFPPomfnx9V/P2Z/PDDLFy2jDU//4zDGLq0a0dItWpFjo/U+bLLGDJuHH5+fgRWrcqMceO8xqR3hSlVSRQ1/oXnC5gILAF+dy1fBKwuyb5Wvjp06HC+Q3+USmUZr8QK2jZ70rbZU2VpG+cY+6ikczTfCPQF0l2J5CAQWp7JSUQuEZHXReQjEbmvPOtWSilVMiVNCtmu7GIARKR6SXYSkTkickREthQq7y0i20Vkp4iMBjDGbDPGDAduBWJL3gSllFLlpaRJ4QMReQMIE5F7gK+BN0uw31ygt2eBiPgDrwJ9cA7JPVBEWrvW9QW+A5aXMC6llFLlSIzHnSfn3FCkF3ANzpFSvzLGLCvhftHAZ8aYS13LVwBPGWOudS2PATDGTPPY53NjzHVF1DcMGAYQHh7eYcGCBSWKvyzS0tIICQmx/Di+oG2zJ22bPVWWtnXv3n29McbrFZkS333kSgIlSgTFiAD2eywnAZ1FJA64CQgElp4jjlnALIDY2FgTFxdXDiGdW0JCAhVxHF/QttmTts2e7NC24ibZSeWPqXoLrAKMMabGeRxTvJQZY0wCkFCiCvSJZqWUskRxQ2eHGmNqeHmFnmdCAOeZQSOP5Uh8+HS0HWXn5DDw0Uc5eOQIAD9s2sSjzzgnwpv+1lscP3Wq2DqOpWWxaf9J8hwlu3yolPpzKGlHc3laCzQXkSYiUhW4DVhcmgqMa5KdmjVrWhJgZefv58ejd93FM7NmkZmVxYvvvMOooUMBeGLoUGoX874s+vkAXZ/9hjve+pHfklNZ/POBighbKWUDliYFEZmPc4jtliKSJCJDjDG5wAjgK2Ab8IExZmsp640XkVmnSvCN2I5OnD7NsCefZMi4cdw5ejR7DxzgyRkzmPzaa/xz8mQ2/PorHdu2JaxGDe576in69ehBuGta0iHjxnH46FGenzOHhJ9+AiAzK4tbH34YY0yBeapTs3JxGMMTH//CsbQsXzZZKVVJlLij+XwYYwYWUb6Uc3Qml6DeC3pAvJBq1Xh1wgQCAgL4bv165nzyCQAN69Vj/H1/PNd3VYcOjH3hBf4zZsxZdcT36MEbCxYQ16kTCT/9RFynToiIzlOtlDonS5OCVS70jubU9HSmvfEGx06eJCc3l2rBwdSvXds97zRARlYWb334ISPuuINX5s0rkCwAWkRHc/jYMU6lpvL5ypWMHDIE0BFJlVLn5os+hTK70PsUPl+5klZNmzJn6lSG3XoruJ4l8RxZ9LX587n5mmsYfOON7E9O5pft28+q59qrrmL+559zJiODxg0bAmfPU+0noiOSKqXcbJkULvQ+hStiYvjqu+94YNIk1m7efNb67Xv2sOm337ilt/Nh8bHDhjH9rbfcI5Tm+9tf/8qcjz+mz1/+UqC8b0wEq0f14L9DO9OqQahOe6mUcrPl5aMLvU+hWePGfOCasMablk2a8I7rFlSAqIgI/vvccwDMnjLFXV4nLIyfPvzQax3581Qn7PL22IhS6s/KlmcKSimlrGHLpHChXz5SSilfsWVSuNA7mpVSyldsmRRU5WCMYej48WzZsQOA3Lw8Bo0cyf5Dh8pcd/4wHPpQnVIVS5OCOm8iwrjhw3n2zTfJy8vjvUWL6NG5M41ct7+eL89hOLo++40Ow6FUBbLl3UcX+sNrdtIkMpKu7dvz/Ntvs/n33wkODOT7jRvJzM7miSFDaNeqFTv37WPiyy9Tq0YNwkJDiQgP576BA1n500+8+eGH+Pn5cVX79gwbMKDAMBz5T10/8fEvdG1WV5+lUKoC2DIpXOi3pNrNkJtv5oYRI3hqxAgubd6c4KAgdu/fz7RZs3jz6ad5+b33GDV0KJe1bMm/Xn0VAIfDwb/ffpt5zz1HaPXqDJ84ke179pBZpZYOw6GUD9kyKajKJSAggAZ169KoQQOmv/UWiQcO4Ofnx5HjxwHYf+gQbVxndW1btODw0aOcOH2aOmFh1HDNQtW2ZUsSDxwgNuYiHYZDKR/SPgVVbtZt2YKfnx9vT5vG2OHD3cNzRDZowK+7dgGw1dUpXatGDY6dPMnptDSMMWzevp3oiIizhuEICvDTYTiUqkB6pqDKTce2bZm/dCn3TphAu0sucZf/8447mPjKK9SqUYOQatVoWK8efn5+PHLnndz3r3/hJ0LX9u1p2aQJ4ByGo2uzuiSdyCCyVrAmBKUqkC2TgnY0Vz75w2vMcw23AXD/QOfI6dGRkbz/738D8K9XXyUqwjnWUo8uXejRpYvX+vKH4VBKVSxbJgXtaLaXnXv3Mv2tt8hzOLiofn26d+7s65CUUkWwZVJQ9nLJxRfz9rRpvg5DKVUC2tGslFLKTZOCUkopt0qVFETkBhF5U0QWicg1vo5HKaX+bCxPCiIyR0SOiMiWQuW9RWS7iOwUkdEAxphPjTH3AIOBAVbHppRSqqCKOFOYC/T2LBARf+BVoA/QGhgoIq09NhnvWq+UUqoCWZ4UjDHfAscLFXcCdhpjdhtjsoEFQD9xehb4whizwerYlFJKFSTGNRSBpQcRiQY+M8Zc6lruD/Q2xgx1Lf8d6Az8DtwJrAV+Nsa87qWuYcAwgPDw8A4LFiywPP60tDRCXGP0XGi0bfakbbOnytK27t27rzfGxHpb56vnFLzNFm+MMS8BL51rR2PMLBE5BMSHhoZ2iIuLsyK+AhISEqiI4/iCts2etG32ZIe2+eruoySgkcdyJHDQR7EopZRy8VVSWAs0F5EmIlIVuA1YXNKddY5mpZSyRkXckjofWAO0FJEkERlijMkFRgBfAduAD4wxW0tRZ7yIzDp16pQ1QSul1J+U5X0KxpiBRZQvBZaeZ506IJ5SSlmgUj3RXFJ6pqCUUtawZVLQPgWllLKGLZOCnikopZQ1bJkU9ExBKaWsYcukoJRSyhq2TAp6+Ugppaxhy6Sgl4+UUsoatkwKSimlrKFJQSmllJstk4L2KSillDVsmRS0T0Eppaxhy6SglFLKGpoUlFJKuWlSUEop5WbLpKAdzUopZQ1bJgXtaFZKKWvYMikopZSyhiYFpZRSbpoUlFJKuVWapCAiTUVktoh85OtYlFLqz8rSpCAic0TkiIhsKVTeW0S2i8hOERkNYIzZbYwZYmU8Simlzs3qM4W5QG/PAhHxB14F+gCtgYEi0triOJRSSpWApUnBGPMtcLxQcSdgp+vMIBtYAPSzMg6llFIlU8UHx4wA9nssJwGdRaQOMAW4XETGGGOmedtZRIYBwwDCw8NJSEiwOFxIS0urkOP4grbNnrRt9mSHtvkiKYiXMmOMOQYML25nY8wsETkExIeGhnaIi4sr7/jOkpCQQEUcxxe0bfakbbMnO7TNF3cfJQGNPJYjgYOlqUCfaFZKKWv4IimsBZqLSBMRqQrcBiwuTQU69pFSSlnD6ltS5wNrgJYikiQiQ4wxucAI4CtgG/CBMWarlXEopZQqGUv7FIwxA4soXwosLUO9S4AlsbGx95xvHUoppc5WaZ5oLg29fKSUUtawZVLQjmallLKGL25JVUqpArbv2cNL771HZlYWx0+cYOXWrYy9914CqpTvn6jpb73F0FtuoXYZvlD+tns36RkZdGjTphwjqzxsmRREJB6Ib9asma9DUUqVUWp6OuNffJEXRo+mUcOGJCQkULN+fRwOR7kf64mhQ0u8bV5eHv7+/meVb9+zhyPHjhVMCikpkJgI0dFQr17ZA/UhWyYF7WhW6sKxat06/tqpE40aNnSXXd66NcYYnp45k1379+NwOBg5ZAhtW7QosO+i5cs5cuwY99x6K4ePHmXsf/7D7ClTeG3+fA4fPcqJ06c5lJLCs48/TpPISIaMG8fURx4hNy+Pkc89R9RFF7H3wAGui4tjUHw8i5YvZ9X69eTm5tKhTRuqBQezcNkyAPpfey039OzJfxcvJj0jgx82bWLqo48S/n//B0OGQNWqkJ0Ns2fDQK/32NiCLZOCUurCcfjYMRrUrQvA8VOnePnDD3n5k0/o26MHuXl5zJ02jaTkZEY9/zzznnuuxPVWCw7mqX/+k6UrV7Jw2TIeveuuAusPHTnCm08/TdWAAAY9/jh9unUD4ExGBq9OmMCJ06cZPnGi+5iDRo7kLx07ckffvu5EREqKMyFkZDhf4Fzu2dO2Zwy2TAp6+UipC0d4nTrsTkoCoHbNmvzzlltYvmkTGZmZtGvVCoDIBg04nZYGwD8nT+ZMRga3XXcdIn+MmmOMKVBv64svBqBhvXr8sGnTWceNjoigenAwAM2iojhw5AgAl7VsiYhw4PBhmkVFERAQ8Mc2hw8XrCQx0XmGkJ8QAAICnOU2TQp695FSyqeu6tCBlT/9RFJysrssz+HA38+PTb/9BkBScjKh1asD8PL48cyeMoVeV15JzZAQDh87BsCvu3YVqPdcCQMg8cABzmRkkJuXx869e4moXx8Afz/nn8WI+vXZkZhITk4OOTk5zm3CwwmoUoXcvDxnJdHRzktGnnJynOU2ZcszBaXUhaNGSAhPP/QQU994g6zsbNLT0mjdogW39O7NjPfeY/CYMTgcDkbfc3YXYpeYGN5bvJjhEyfSqmnTUh23Yf36TJo5k30HDxLfowe1w8IKrK8dFsatffoweOxYAAb87W/UrlmTmEsuYcHSpezat4/Rw4ZRd/Zs5yWjgABnQpg927ZnCYAzg9rtBcQDs5o1a2YqwooVKyrkOL6gbbMnbVvZJCUnm2FPPll+FR45YsxPPzn/PYfK8rkB60wRf1/18pFSSpVVvXrQsaO9zxBcbJkUlFKqLCLCw3lj0iRfh1EpaVJQSinlpklBKaWUmy2Tgo6SqpRS1rBlUtCOZqWUsoYtk4JSSilraFJQSinlpklBKaWUmyYFpZRSbpVm7CMRqQ7MBLKBBGPMPB+HpJTtrd28maUrVzJxxAiAAnMOeDPmhReY9uijZT5mzdBQWkRHk5mVxUNTp5KVnU1eXh73DhjAVR068Nvu3UybNQt/Pz/8/f2Z+MADRDZoULr4XRPbrM3KYunPPzNxxAjGvPACD//jH2Vqo2f8AK/Om8dnCQk0btjQ/cDb2s2b2fDrr6zesAERoWlkJD9s2kSjhg1pGhnJ9j17CKlenR83bXLPAXF9XBy1AgIwxvDsm2+6t5n80EPUDA11Hrykk/VYOKmPpWcKIjJHRI6IyJZC5b1FZLuI7BSR0a7im4CPjDH3AH2tjEsp5V1pEkJe/kihhazbsoUdiYkA+Pv7M+H++5k7bRovjR/Pc7NnA1C3Vi1mTpzInKlT+Ue/frw2f37pAp0/H6KioFcvGDAAXCOkliT+/G1KEn9eXh639unDm08/XWCb1Rs28OnXXzN78mSmPvIIvycm8ubTT3MqNZXMrCzenjaNa7p2JbBqVWZPmcLsKVO4sVcvAL7fuLHANnMXLjy7TVFRzuXi2n6u7c6T1WcKc4FXgHfzC0TEH3gV6AUkAWtFZDEQCWx2beb901JKlYsnZ8ygWnAwBw4f5lRqKjPGjqV2WBjxw4ez5PXXWbt5M3M+/phqQUHsT05m6C23cE3Xrrw2fz4HjxzhdFoavbt1Y+/Bg3y/cSMOh4NhAwbQrmVLFn/zDUGBgXyybBmzJk0iIjwcgMCAAPxcw1LXrVXLHUtAQIB72stFy5ez8MsvWfzDDyQmJRHfowdrfv6Zm/75T0YNHUp0RATk5PDkCy9QrVkzDlSrxoFq1bjk++8hJYX4J5/krcmTSU1Pp9dddxFYtSonTp+mTlgYc6dN439ffMF7ixfT8dJLaREdzRerVnE6LY3s7GwGXn89g2+8kXlLlgAwffZsnhgyhP3JyST8+CP7k5P5dt06dxtz8/IY/tRTzJo0iZzcXHLz8jidlsZfOnYE4K8dOzLxpZcYMm4cBw4fpn3r1uzeu5eT6en06tqV4RMncvzkSapUqQIpKTz5wgsENW/OwWrVyPLz45axY1m4eTOnMjOZMX489WvXrpBJfSxNCsaYb0UkulBxJ2CnMWY3gIgsAPrhTBCRwM+c4wxGRIYBwwDCw8NJSEgo97gLS0tLq5Dj+IK2zZ5K2rYd+/dz6NAh97YnU1M5efIkfg4HEfXq0b9bN5b99BOvzp1Lt5gYMjIySEhIYMf+/ezZv5+Rt99OTm4uz735JlWys0lMTOR0ejoDevYkKTmZb1av5r6bbiIjK4unX3mFMXfeyWVNm1IvLIzYSy5h1apV7ljmL1tGl0suKRB3Vk4Or370Ebdfcw0JCQn89ttvZGdn07dLFzZs3868RYu4OCKCbjExvPzOO/Tv3p2Tqan4tW9PRPXq9I+IYMHvv/NrRgYJa9eSkZHBmjVrSEtLI/XMGa689FKuuPRSJr39Ni/PncuZjAzy8vK4sWtXvt+8mSNHjzJ1+HBOnznDlLlzaRMZSXitWogII/r3J+nIEb5ZvZpbr76a+cuWudsYWbcuBvh7r16sWrWKvJwcElat4syZM+zasQO/zEyMMdSuUYO/9+rFzI8/Zu0vv/BQ//68/PHHbNqyhbvj41mxfj3L160jYe1aktu3J7J6dW6JiOCDHTv4PCaG2666ioTffmPm3LnEtW8PZ87A1KngeZbj7w9r10K1auXyu+WLPoUIYL/HchLQGXgJeEVErgOWFLWzMWaWiBwC4kNDQzvExcVZGSsACQkJVMRxfEHbZk8lbVud339nz9Gj7m33HTrEso0bqV2zJn179qR9mzakA/uTk4mLi+P5BQuIi4uj+ubNJB49ytVXXw3AgoQE2l1+OduTk4kIDycuLo6vvvuOv3TpQvfu3QH4YOVKYlzbNG7YsEB8s/73P1o2a1ZgSsyc3FwefeYZHr7rLnp06QLAqbw8TqalERcXR8369dl1+DA1Q0O5rndvlm/cSPvYWJb9+CO1v/qKvrt30/74cX5r0YLlkZHEdezI84sX0z42ltrffoufvz/9evemfZs2zFy0iBphYdQPD6f6b78RFxfHrwcP0viii+jZsycAz8+fT/TFFxMWFkZ0RESBNl5xxRUs27CB0Bo1iLn8ciI3buTE6dPuNs789FPiunXjf8uXc3Hz5sR16cLptDQu+vJL4uLiWL5pEz9u2kRISAgRDRvSPDqauLg4TjscrNm6lbiOHVk+cSLxrjbtaNGCenl5xD31FDlhYfy+Z4/zWCkp0L9/wZnegoNh795yO1Pwxd1H4qXMGGPSjTF3GWPuK66TWZ9oVqpkoi66iB2JiWTn5ADODtL8yWiKm5ls+5495OblkZ6RwbGTJwlzdYbmz0wWddFFbN6+HWMMp9PSOH7qFGE1ahScmQxY8Pnn7Dt0iEcGD3aXORwOxv3nP3Tv3NmdEPJ5/oEIrVbNHb/Jj79VK+jaFQkMhBo1qOtwcKxuXbJdk+Ss3byZxg0bciotDYcxpGdkkJaeTmDVqgWOE163LgeOHCEnN5fklBSysrOJuugi/Pz83O+HZxtz8/LcbYwID2fvgQPk5OZyKCWFasHBBFSpQo2QEL5bvx6A5WvWcHnr1gCcTksjxDVz3EX167Nz714Aftu9mwb16jn/oHu0iSpVkLvvdv+hd3869eo5J/EJDnZuFxxc7pP6+OJMIQlo5LEcCRwsTQU6R7NSJVMjJIS7b76ZoePHUzUggJohIUwcMcLd4Xsu9WrXZuT06Rw4fJgRgwa5r/vna9W0Ke1ateIfo0bhMIbHBg/Gz8+PLjExPDd7NqvWrWPsvfcyffZsLmvZkqHjxwMwa9IkvvnxR1atX8+xkyf5PCGB5lFRjB427KwYAgICuPvmm3l02jRn/8WGDc74jx2Djz6CgACCk5O5fOtWho4fz5Hjx/l+wwb69ujB2s2befX990k/c4ZeXbsWSIIAjRs2pE7Nmlx9551k5+QwKD6egIAAIhs0YN2WLTz+7LNMHzkSf39/bn34YbKys7m4cWMOHD7MXzt14stVq+g5eDBRERFcfskljHvxRZJTUjhx+jS3P/YYAQEBpJ85w+bt20k8cICH/vEP93EPHzvGXWPGkJGVReyllzoDatLE3Sa2bIHGjb1/MAMHOvsQLLr7qCJmSYsGtngsVwF2A02AqsAmoM351N2hQ4cyzj9UMpVltiQraNvsyeq2/fTLL+apl1+29BhFKY+2lSR+X7SxsvxO4quZ10RkPrAGaCkiSSIyxBiTC4wAvgK2AR8YY7aWsl4dJVUppSxg9d1HA4soXwosLUO9S4AlsbGxZ8/krZQqs45t29KxbVtfh3HeShK/3dtoFVsOc6FnCkopZQ1bJgWjdx8ppZQlbJkU9ExBKaWsIcbL/cl2ISIpwF6PoppA4UxRXFnh9fnLnuV1gaNlDNdbHKXZRttWsrZ5/mzXthVetlPbvJVr2ypf28KMMd7vZS3qtiQ7voBZpS0rvD5/udA2Rd6+VZbYSrONtq1kbSvUTlu27VxtrextK64t2rbK27b8ly0vH52Dt+ExiisrvH5JEeVlVZL6zrWNtq1kbStpPCXli7YVXrZT27yVa9vs0TbA5pePKoqIrDPGxPo6Dito2+xJ22ZPdmjbhXamYJVZvg7AQto2e9K22VOlb5ueKSillHLTMwWllFJumhSUUkq5aVJQSinlpknhPIjIDSLypogsEpFrfB1PeRKRS0TkdRH5SETu83U85U1EqovIehG53texlCcRiRORVa7PLs7X8ZQnEfETkSki8rKI3OnreMqTiHRzfWZvicj3vo4HNCm4icgcETkiIlsKlfcWke0islNERgMYYz41xtwDDAYG+CDcUill27YZY4YDtwKV+tY5KF3bXEYBH1RslOenlG0zQBoQhHMiq0qtlG3rh3Ma3xwusLYZY1a5/r99Brzji3jPUtan6y6UF/AXoD0FJwTyB3YBTfljQqDWHuufB9r7OvbybhvQF/geuN3XsZdn24CewG04k/n1vo69nNvm51ofDszzdezl3LbRwL2ubT7ydezl2TaP9R8ANXwduzEX3hPN580Y8y1wvFBxJ2CnMWa3MSYbWAD0E6dngS+MMRsqOtbSKk3bXNsvNsZcCQyq2EhLr5Rt6w50AW4H7hGRSv37X5q2GWMcrvUngMAKDPO8lPJzS8LZLoA8KrnS/n8TkcbAKWPM6YqN1DtfzNFsJxHAfo/lJKAz8E+c3zprikgzY8zrvgiujLy2zXU9+iacf1jOeyIkH/PaNmPMCAARGQwc9fhDaidFfW43AdcCYcArPoirPBT1/20G8LKIdAO+9UVg5aCotgEMAd6u8IiKoEnh3MRLmTHGvAS8VNHBlLOi2pYAJFRsKOXOa9vcPxgzt+JCKXdFfW6fAJ9UdDDlrKi2ncH5h9POivydNMZMrOBYzqlSnz5XAklAI4/lSOCgj2Ipb9o2e9K22ZNt2qZJ4dzWAs1FpImIVMXZSbnYxzGVF22bPWnb7Mk2bdOk4CIi84E1QEsRSRKRIcaYXGAE8BWwDfjAGLPVl3GeD22btq2y0bZV3rbpgHhKKaXc9ExBKaWUmyYFpZRSbpoUlFJKuWlSUEop5aZJQSmllJsmBaWUUm6aFJSqACISLSK3+zoOpYqjSUGp8+AaKbc0/3+icY7OqlSlpklBqRJyfdvfJiIzgQ3AbBHZIiKbRWSAaxsRkecKlwPPAN1E5GcReURE2ojIT67lX0Skua/apZQnfaJZqRISkWhgN3AlzqGQhwO9gbo4x7bp7Frnrbwl8Lgx5npXXS8DPxhj5rnGwvE3xmRUaIOU8kLPFJQqnb3GmB+Aq4D5xpg8Y8xhYCXQ8Rzlha0BxorIKCBKE4KqLDQpKFU66a5/vY2Pf67yAowx7+Oc9jQD+EpEepRDbEqVmSYFpc7Pt8AAEfEXkXo45+X96RzlqUBo/s4i0hTY7ZqwaTFwWUU3QClvdOY1pc7PQuAKnBOwG+AJY0yyiBRVfgzIFZFNwFwgCLhDRHKAZGCSD9qg1Fm0o1kppZSbXj5SSinlpklBKaWUmyYFpZRSbpoUlFJKuWlSUEop5aZJQSmllJsmBaWUUm6aFJRSSrn9P9AbD864EbkxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=table_fel[table_fel['vertices'] < 100000].plot.scatter(x='roots',y='leafs',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,label='small',\n",
    "                          title='Graphs from FELINE paper, Table 1')\n",
    "ax=table_fel[table_fel['vertices'] >= 100000].plot.scatter(ax=ax,x='roots',y='leafs',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,c='red',label='large',\n",
    "                          title='Graphs from FELINE paper, Table 1')\n",
    "for row in table_fel.itertuples():\n",
    "    ax.annotate(row[0], xy=(row.roots,row.leafs),\n",
    "                xytext=(-3,-3), textcoords='offset points',horizontalalignment='right',\n",
    "                family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets from FERRARI (`table_fer`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"FERRARI: Flexible and Efficient Reachability Range Assignment for Graph Indexing\" (2013)  \n",
    "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.365.2894&rep=rep1&type=pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- ![Table 2. Datasets used](images/ferrari/FERRARI_arXiv-table_2-datasets_used-with_caption.png) -->\n",
    "![Table 1. Datasets used](images/ferrari/FERRARI_paper-table_1-datasets_used-with_caption.png)\n",
    "\n",
    "- $|V|$ - number of vertices (nodes)\n",
    "- $|E|$ - number of edges\n",
    "- $|V_C|$, $|E_C|$ - number of, respectively, vertices and edges in the _condensed graph_, where maximial strongly connected components were collapsed into \"supernodes\", i.e. directed acyclic graph (DAG) created from given graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>type</th>\n",
       "      <th>vertices</th>\n",
       "      <th>edges</th>\n",
       "      <th>edges/vertices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graph</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arXiv</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6000</td>\n",
       "      <td>66707</td>\n",
       "      <td>11.117833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6793</td>\n",
       "      <td>13361</td>\n",
       "      <td>1.966878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>9000</td>\n",
       "      <td>40028</td>\n",
       "      <td>4.447556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>38811</td>\n",
       "      <td>39816</td>\n",
       "      <td>1.025895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseer</th>\n",
       "      <td>large</td>\n",
       "      <td>693947</td>\n",
       "      <td>312383</td>\n",
       "      <td>0.450154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cit-patents</th>\n",
       "      <td>large</td>\n",
       "      <td>3774768</td>\n",
       "      <td>16518047</td>\n",
       "      <td>4.375911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseerx</th>\n",
       "      <td>large</td>\n",
       "      <td>6540401</td>\n",
       "      <td>15011260</td>\n",
       "      <td>2.295159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go-uniprot</th>\n",
       "      <td>large</td>\n",
       "      <td>6967956</td>\n",
       "      <td>34770235</td>\n",
       "      <td>4.990019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GovWild</th>\n",
       "      <td>RDF</td>\n",
       "      <td>80222880</td>\n",
       "      <td>23652610</td>\n",
       "      <td>0.294836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yago2</th>\n",
       "      <td>RDF</td>\n",
       "      <td>16375503</td>\n",
       "      <td>25908132</td>\n",
       "      <td>1.582127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twitter</th>\n",
       "      <td>social network</td>\n",
       "      <td>18121168</td>\n",
       "      <td>18359487</td>\n",
       "      <td>1.013151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web-UK</th>\n",
       "      <td>web graph</td>\n",
       "      <td>22753644</td>\n",
       "      <td>38184039</td>\n",
       "      <td>1.678150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       type  vertices     edges  edges/vertices\n",
       "Graph                                                          \n",
       "arXiv          small, dense      6000     66707       11.117833\n",
       "Go             small, dense      6793     13361        1.966878\n",
       "Pubmed         small, dense      9000     40028        4.447556\n",
       "Human         small, sparse     38811     39816        1.025895\n",
       "citeseer              large    693947    312383        0.450154\n",
       "Cit-patents           large   3774768  16518047        4.375911\n",
       "citeseerx             large   6540401  15011260        2.295159\n",
       "Go-uniprot            large   6967956  34770235        4.990019\n",
       "GovWild                 RDF  80222880  23652610        0.294836\n",
       "Yago2                   RDF  16375503  25908132        1.582127\n",
       "Twitter      social network  18121168  18359487        1.013151\n",
       "Web-UK            web graph  22753644  38184039        1.678150"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Table 1: Datasets Used from FERRARI (table_fer) { form-width: \"25%\" }\n",
    "\n",
    "csv=u\"\"\"Graph;type;vertices;edges\n",
    "arXiv;small, dense;6000;66707\n",
    "Go;small, dense;6793;13361\n",
    "Pubmed;small, dense;9000;40028\n",
    "Human;small, sparse;38811;39816\n",
    "citeseer;large;693947;312383\n",
    "Cit-patents;large;3774768;16518047\n",
    "citeseerx;large;6540401;15011260\n",
    "Go-uniprot;large;6967956;34770235\n",
    "GovWild;RDF;80222880;23652610\n",
    "Yago2;RDF;16375503;25908132\n",
    "Twitter;social network;18121168;18359487\n",
    "Web-UK;web graph;22753644;38184039\n",
    "\"\"\"\n",
    "csv_stream = io.StringIO(csv)\n",
    "\n",
    "table_fer=pd.read_csv(csv_stream,sep=';',index_col=0)\n",
    "table_fer.insert(loc=3,column='edges/vertices',\n",
    "               value=table_fer['edges']/table_fer['vertices'])\n",
    "table_fer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2wGemINAOCNNaf6KU+qo681Wokbszb0/sxtMl2hgsuVsAOHToEE899RR2dnY4Ojry0UcfMWnSpAqPmz59OrfccgvBwcEEBQXRoUOl1iMXQtio3NxcNm/ezI4dO3BycmLcuHH07NkTO7va7RdU3XcMX2C8Cyj6oldK2QP/A0YCMcBupdRvwD5gnlLqduDras5XkfFBfgxo401Mahb+DV0tDgoAo0ePZvTo0cW2RUZGFv09c+ZMZs6cWeq+HTt2lJpmRkYGYFyD4vDhwxbnUQhRe7TWHD9+nJUrV3L58mWCgoIYMWJEnVmASGmtq/cESgUAfxTeMSil+gMLtNajTY+fNT01D9iltd6slPpRa13qJbZSajYwG8DX17fXsmXLiu1v0KABpU2uV1MKCgqwt7evlXOfPn2atLS0Wjl3ZWVkZBQN1hOlkzIqn62XT1ZWFqdOnSI1NRU3Nzfatm1LgwYNrHqOispo6NChe7TWwWXtr402Bj8g2uxxDNAX+BhYoJS6A4gs62Ct9UJgIUBwcLAuuUrRsWPHanwSO3O1MYleIRcXF3r06FEr564sWX2rYlJG5bPV8snLy2Pbtm3s2bMHe3t7Ro8eTZ8+faql2sjSMqqNwFBanyuttT4MVFwRT/nTbgshRF1z6tQpVqxYQWpqKl26dGHUqFG1egFbkdoIDDFAc7PH/kBcVRIob9ptIYSoKy5dusSqVas4fvw43t7e3H333TYxNX5tBIbdQFulVCsgFpgK3FEL+RBCiGpRUFDAjh072LRpE0ophg8fTv/+/S1uf7xy5Qo7duxg8ODB1booV3V3V10KhADeSqkYYL7WepFS6iFgFcbuqou11keqmK5UJQkh6qRz584RHh5OcnIyHTp0YPTo0Xh6elqUZuGcSWvXriU7O5uWLVtWayebag0MWutpZWwPB8ItSLdOVyW5u7sXdS8VQtwY0tPTWb16NYcPH6Zhw4bccccdtG3b1uJ04+PjCQsLIyYmhhYtWhAaGlrtcybVmZHPVXE93TFordFa1/qAFiHEtTEYDOzatYsNGzZQUFDAkCFDGDBggNWqelasWMHFixdrdM4kmwwMVr9jSEqCyEgICAAfH6skCca+xBMmTCA1NZW8vDxeffVVJkyYQGRkJGPHjmXo0KHs2LGDX3/9la+++opvv/2W5s2b4+3tTa9evXjyySc5c+YMDz74IElJSdSrV49PP/1URkQLUUdERUURHh5OQkICbdq0YezYsXh5eVmUZuGcSYGBgbi5uXHrrbfi4uJSo3Mm2WRgsKqlS2HWLHBygtxcWLQIppVaA1ZlZU2jDXDixAk+//xzPvzwQyIiIvjpp5/Yt28f+fn59OzZk169egEwe/ZsPv74Y9q2bcuff/7JnDlzWL9+vVXyJ4S4NpmZmaxdu5b9+/dTv359pkyZQocOHSy+mjefM2nIkCGEhITQsGFDK+W68mwyMFitKikpyRgUsrKMP2B8PGKEVe4ctNalTqMN0LJlS/r16wfA1q1bmTBhQtEVwS233AIY7zi2b9/O5MmTi9LMybHOYkJCiKozGAzs2bOH9evXk5uby4ABAxg8eDBOTk4WpZubm8umTZvYuXMnTk5O3HzzzfTs2dNKua46mwwMVqtKiow03ikUBgUAR0fjdisEhvKm0TafE6WsaUkMBgOenp7s37/f4rwIISwTFxdHWFgYcXFxBAQEEBoaio+Vqp7XrFlDREREnZkz6cZu8QwIMFYfmcvLM263gspOoz1w4EB+//13srOzycjIKFr5rX79+rRq1YoffvgBMAaQAwcOWCVvQojKycrK4o8//uDTTz/l8uXL3Hbbbdx9990WB4WUlBRSUlIAGDx4MPfeey8TJkyo9aAANnrHYDU+PsY2hVmzjHcKeXnGx1a6CqjsNNq9e/dm/PjxdO/enZYtWxIcHFw0qda3337LP/7xD1599VXy8vKYOnUq3bt3t0r+hBBl01qzf/9+1q5dS1ZWFn379iUkJAQXFxeL0s3Ly2Pr1q1s27aNwMBA7rjjjqLVHOsKmwwMVu2uOm2asU3Bir2SCscweHt7lzmNdsmps5988kkWLFjAlStXGDx4ME888QQArVq1YuXKlRbnSQhReQkJCYSFhREdHY2/vz/jxo2zyhK7J0+eZMWKFVy6dImuXbsycuRIK+TW+mwyMFi9u6qPj1W7qV6L2bNnc/ToUbKzs5kxY0atNjwJcaMqud7y+PHjCQoKssrYgQMHDvDrr7/axJxJNhkYrkdLliyp7SwIccMqud5yr06dGN6sGa7+/mBBUCgoKCAtLQ0vLy86duxIdnY2wcHBtbZmS2VJYBBC3NCSkpJYsWIF586do2nTpkxt2BC/GTMsHtt09uxZwsPD0VozZ84cnJyc6Nu3bzW8AuuzycBQURuD1rpGho3XJdW9Ep8Q1aaaZh6oSMn1lkNDQ+nVogV2rVpZNLbp8uXLrF69miNHjtCwYUNCQ0Pr/B1CSTYZGMprY3BxcSElJYVGjRrdMMFBa01KSorFvSWEqHHVOPNAWQrXW161ahVpaWl0796dkSNHGruJ7t5t0dimxMREFi1aVDRn0sCBA3FwsL2vWdvLcQX8/f2JiYkhKSmpVs6fnZ1dK1/QLi4u+Pv71/h5hbhmppkHXmvblv5JSQxLS+PMI48w6fvv2fjVVzTw8OC78HDS0tOZffvtxQ6NTUjg5f/9j09efrnM5HcfOkT4pk3Mf+ghABKSk3n6nXcY1a0b361bR1CbNsyaOZPj0dE88vrr/Pf556l3jWObrly5Qr169fDx8aFXr14EBwdbPGdSbbruAoOjo2OttvZv3Lixzq+7LESdYJp5IOjiRfZ7eTHswgX2N2pEn5YtOXD8OIN792b/8ePcZoUunfn5+ezYsYPY2FiiPD1p1qwZ48eP53h0NN+vWGEMCq6u4OpapbFNmZmZrFmzhpMnT/Lggw/i5ubGqFGjLM5vbbvuAoMQN5IT587xf19/TXZODgUGA638/Xnu/vtxtHL1xduffcZ9kyfjZRp4eS2Onz1LZlYWvTp3Nm4wXZ33TElhmelibr+HBzMmTGD3sWMM7t2bw6dOMXHUKGbNmwdAK39/BpgGiqZfucKz777L+dhYxoWEMN00x1hJhestxyUkUK9ePR588EHe+fxz1u/caSy/wqBQqBJjm0rOmXTTTTdV64pqNc0mA8P1tB6DENcqPTOT5997j3efeYbmTZsCsO/oUQwGg9XP9fR991X6uQUFBaU2tp44d47ElJS/AoNp5oGms2ZxycWFbDc3knv1ot+QISx57TUSkpNpWL8+7yxaxGevvoqHmxvvLFrE0XPnaNepExcSE/n0lVdwcnRk+pNPMnbQILzMVkrLzMzk7LlzLFmyhEaNGnHbxIkkLVtG/RMnIDub33ft4rn778ettOmsyxnblJuby5dffklcXBytWrUiNDQUb2/vSpePLbDJwFDXV3AToiZsiYhgSJ8+RUEBoEenTmiteeXDDzkTHY3BYOCpWbPo2q5dsWOXr1tHYkoKf58yhYTkZJ77z39Y9NprfLR0KQnJyaRevsyFpCTeevJJWvn7M2vePF5//HHyCwp46p13aNmsWbEr9eXr1rFlzx7y8/Pp1bkz9Vxd+WXNGgAmjR7NrSNG8M1vv5GZlcXOAwd4/Z//xLdRo6Kr887/+Q+bHnuMRqdOYWdnh51S/HnwIAF+fmz4808ee/11ALKys+ncsiUAAX5+RV/qbVq2JDYxkf9+8w1RFy4Q6OvLxfh4UlNTGXbrrfTv35+4xYtx2b4dFi6Etm15cfJkPvnxR3y8vAju0qXC8i4MeE5OTvj5+dG/f386d+58XXZyscnAIISAhJQUmpiuVC+mpfHU229zKT2d8cOGkV9QwBdvvEFMfDxz//1vvn3nnUqnW8/VlQUPP0z4pk38smYN/7znnmL7S7tSB7iSlcX/XnyR1MuXeWD+/KJzTn/qKQb37s2d48cXBaNifHwIGjiQL9auZeLo0QB0bN2apX/8wezbb+dUZORfbQDAunXrAIiMjeVKVhZOTk6cPn8ev8aNuXvcuKL1lrt26MC5y5fp268fDqmp7H7nHTo4OkJaGhQU4P3ee7y/bh0P/+9/vPrYY3Qqp/v7/v372bBhA3fddRc+Pj6EhoZWujyrRTV38ZXAIISN8m3UiLMxMQB4NWjAotde44X33ycrO5vupnp4/yZNuGyau+vhV1/lSlYWU8eNK3aVW3IMTKfWrQFo6uPDzlJm8y3tSh2gW/v2KKWITUigTcuWRXXubVq2JNa0DklZenbqxJsLF9K9fXsAgjp2ZOH339OjY0eeuPdeHn39deMSuEoR0q0bHYCmjRvz8ocfEhUXx6ibbmLDunUcPnwYT09Ppk2bRrt27fANCOC+55/HKTubBo0bM3/Xrr9O6uCAX0YG78ydyxNvvsm7zzxDYPPmxfIVHx9PeHg40dHRNG/evG7cHdRAF18JDELYqIG9evH5zz9z6/Dh+JsmeCswGLC3s+OAqTdPTHw8HqZpnP/7/PNFx27atYuDJ04AcPTMmWLplhc0oPQr9bPR0dib1i33a9yYU5GR5OXlARif4+tL9IUL5BcUlPpa2gUEsP/XX4se9w8KYt8vvwAQ3KVLsaqejRs34ufry5J//avYesvxBQUMHjyYgQMHFgWlsYMHM3bwYOMVdsuWkJ8PwCv79hl7IAUE0NrHh1//97+r8rRmzRp27NiBq6trja63XK5qXlyskAQGIWxUfXd3Xnn0UV7/5BNycnNxdnKiibc3k8eM4f2vv2bms89iMBh45u9XN8X1Cwri699+44H58+kQGFil85pfqd8ybFixBl8AL09Ppowdy8znngPg9tBQvBo0IKhjR5aFh3MmKopnZs+mkacnf3/hBR6bMYMubduSX1DAjGee4c1//rNYu0lZoqOjCQsLIyEhgdatWzN27FhOREVx77x5ODk64uLszGuPPYZn/fqVnmLffNYEOzs7evXqxbBhw2p0veVyVfPiYkW01jb706tXL13XbNiwobazUKdJ+VSsLpdRTHy8nv3CC1ZL72x0tL7zqad0fn6+XvzTT/qzH36o8JhVq1bpX3/9VS9YsEC/++67+siRI9pgMGittY5LTNQ5ublaa62XhYXpD775pvjBiYla79pl/K1L7krUX375pT59+rTWWhelWackJmrt6qo1/PXj6nrV66noMwRE6HK+W+WOQQhRa1r5+zOgZ0/+/fnnHDp5EldnZ7bv20d2bi5Pz5pF9w4dOB0Vxfz//peGHh4U5OWRlpJCx6ZN8WzalF0nT7Lvyy8Z2LMns2+/naZmV82Ojo5Xd5s174ZqasDN9fNj89GjRXMmZZmuxpVSFo8TefDll3n4zjvpEBjIlogI5r33Hpu+/hqlFP/54gvatmxJ4sWLDOrVi7YBAdzywAP8/vHHxdLYd/QoP69ZwyuPPlrti4sVssnAIOMYhKgdfr6+5U5DcS1mTZzIrQ89xIKHHqJL27a4urhwNjqaNxYu5NNXXuG/X3/NrFtv5czRo/yxcyee7u78/e9/5+8vvcS377yDh5sbD8yfz4lz52hvGiiXcukSy8LC+HjBgtJPamrAPdm+PeFDh5LWoMFV6y1bY5xIUMeO7D92jA6Bgew/fpyOgYGciYqiTcuW7D9+nCljx+Ln61u1AquGxcVKssnAoGUcgxC1y4rdJR0dHWni7U3zJk14+7PPiIyNxc7OjsSLF8nKyuLoqVM0zMvD3d2d4QMHEhsfj6OLC408Panv7g5A1/btiYyNpX2rVmRcucKTb73FC3PmXNX+UZR3UwPuZQcHnLOzuef332kxZw6YrbdclXEizZs04eFXX+Xrt98G4NMffqCxlxc9Onbkh5UrmTpuHCcjI5k8Zgz7jh2jRbNmpKSm4ufrywvvv89tI0fSo1Onv7J48SLP/PvfODs5FbsLKlLNi4vZVVvKQojr09Klxh4+I0cafy9dapVkIw4fxs7Ojs/feINn77+fnOxsPvjgA+wNBpoGBPDQQw+RZqrmaVi/PimXLnE5IwOtNYdOnCDAz4/snBz++cYbzJo06apBfWBcb3nj6tXsDwoCoOeePdz/ySe0SEw0BjozJceJzJo3j4mPPMJXy5cXjRN5/fHHefPTT/GsXx8fLy9OmdJYv2MHI2+6ia7t2nHk1Cny8vNRQM/OnTlw/DhHTp2iSyn5K7T455+ZNHo0H86fT5NaWF3SJu8YhBC1xErdJdMzM3nspZcgO5sTSUlo4PsVK7h4+TIzn3kGJ63JzMzEy8uLFx95hGfee4+DUVHY29lxJSMDOzs7Hp8xg+lPPUU9FxeG9u1L+1at+PLXXzkZGcnnP//M5z//TL+gIP4+eTLw15xJqampBPv4EATYFTbhljKDalXHidwcEsLvGzcyasAAAps3LxqQ5+XpyfqdO+kQGIhXgwYkp6ay79gxgjp2LLN8ouLiuGPcOAC6tmtHVFxcpcvWGiQwCCEqz0rdJT1++41FH3wATk7M6taN12fMwPPOO9m4cSN//vknLi4uPHD77fTo0YP8ggI2f/MNAA+98gppmZkADOvXjw1//lmsGmbGrbcy49Zbi50rLS2NlStXcvz4cbwaNeKuu+4isF07WLOm3Abcqo4TGRQczEdLl5KVnc3NQ4cWpRPUsSNf/PwzD991FwA+Xl6s2baNlx55pMzyadG0KUfPnKF506YcOXWq0uVqLRIYhKghJyMjef+rr8jOySEvP5+RN93E0TNneOOf/yQ2IYFT588T0qePRee4nJHBpt27ucXsi8mqx17jegXFlLzrMBh4delSGkRHg8FA+KFDfPP22zTx8WHOSy/xyF13MeOZZ+gQGMiFpCSys7OZNW8eD915J9v37eNkZCTNmzblX08/zdI//mD1tm0UGAzcOmIEt40cyffh4SxfvRqvRo0I6NaNwMBACAyssAG3quNEHB0c6NW5M5t27+bZ2bOL0unZqRPf/PZbUdVWUMeObPjzT9q0aFFmEc287Tae/fe/+XXt2tLbGKpbeX1Z6/qPjGOwPTdq+aRnZupJjzyio+LitNbGPvLb9u4t2r/r4EG94L//1VpbVkaWjDOo9LFLlhj7ztevb/y9ZEnVTrRrl9YNGmgNOqlRIz3uttv0vY88oh94/HH9Q1iYfvS11/TSsDC9csuWonENN99/v/HQgwf1/c89V5TU8++9p/ceOaK11vpMVJR+8OWXtcFg0KdOn9Z/mzNHp6al6V/XrtWzX3ihbo5LqCYyjkEIG7B5924G9+5d1MNFKcVNPXoU9Vv/5rffOHL6NLPmzSOkW7dixxbOXJqbm0tCSgpz77uPnp078/Xy5WyOiCAzK4uBvXoxZ9o0vvntN46eOcOsefOY8be/4e3pyZNvv42fry9ZOTk8etdd9O7alVseeICxQ4aw5/BhGnl68vZTT1117Pm4OFZt2YKLszND+/b9a70DS7tLBgSQZzCwefhwtt90EzmnTzNp717ChgzhbFwcD0ydyuc//4yHmxsThg+vdLKno6I4c/48tz7wAJmZmRRoTZxpHqcenTrV/nQWNkQCgxA1wLyHS2nuHD++aBnKjRs3XrX/SlYWH86fT2xCAk+9/TZL/v1vJo0ezV0TJmAwGJj57LP8bcQI7hw/nrPR0UVjDeb+61+8+cQTdGvfnrn/+hdf/vILvbt2Jb+ggGF9+zJn2jQemD+f0+fPX3XsHU88waevvoqbq+vVffcr0V2ytHUZtNacSElh5dy5pOXn0+3oUc4kJtJz5ky+PnaMgpgYOgQGkp2TQ1RcHM+YVcmAsWtrgVleHB0cyC8owGAwkH7xInYFBQS3bMmgQYPo27cvrq6unDp/vmgeJ1E5EhiEqAG+jRpxOiqqUs/Nyc0tWrHsoenTAehsGszp5+tL+pUrAKzdsYNf1qwpmtE0PjmZxl5eRF24wN1z5+Lk6EhkbCzb9+0j0N+fo2fP4uzoyKx587C3syPqwgXe+vRTzsfF8fXy5cy+/XYuZ2Qw/amncHV2xqtBA9769FNS09JIuXQJt3r18PTw4JVHH8XF2bnU+nzzdRl6durEqm3b+O+8eaSmp/PSf//LyK5dOXv2LD4+PswcMICWoaH88cMPMGECXfLzuZKdDUD7wEAMWuNQIrC0bdmSlLQ0nnzrLe6//XYG9+7NR0uX0tTbG+eMDDoGBHAyPZ3za9fyw+bNvG8qR1E1dSYwKKUGAdMx5qmT1vqmWs6SEFYzuHdvPv/5Z/42YkRRddKO/fuL9js6OhbNPOrs5MSi114r2hd14ULRDKgXkpJwN3WD/HDJEn754AOcHB2Z+eyzoDX7jh0jOzeXL998E6UUD778Mmnp6Xz51lt8tHQpzRo3ZsLw4YybPZuvly9n8euv8/rHH3MuNpaYhASSL13ipYcf5qYePcjMysLN1ZU7n3qK3Lw8lrzyCt/+/ju/rl1Ln27d2LZvH4tffx2DwcA9zz3HsL59gb/WZVBK0bltW55/7z0uJCTQwdubmJgYRo4cSd++fYvuJhb17g3Ac/ffX/Sa55SYRrpwmgg3V1cemTKFkJAQMjMzSUtOZrFpEZ/ExER8fHyuqjKqSnWUMKrWwKCUWgzcDCRqrbuYbR8DvA/YA59prd/UWm8BtiilbgV2V2e+hKhp7vXq8epjj/HGwoXk5OYW9Uoq1LZlS2Li43nyrbfoaVoPwZyrszMPvfIKSRcv8uS99wLG7pozn32WAD8/XF1cAEhMScGrQQOefOstpowdy98nT+axN97g7y+8QGxCAgN69GDC8OHkFRRwISmJB+bP53xsLC4uLmRlZ9OyWTNe//hjmjVuTHpmJvVcXTl25gz+TZowa948cvPy6Nu9O6ejojgbHc19pqm8M7OyiE9OBv5alwGggbMzp8+dw83RkZ7dujF69Gjq169vUVlqrYmIiGDdunXk5ubSunVrGjZsSOPGjS1KV/yluu8YvgA+AL4q3KCUsgf+B4wEYoDdSqnftNZHTU+5A6j8ArNC2Ij2rVrx4fz5xbbdNWECYLwS/vyNNwBKbWNo36rVVSufPTVr1lXPy7hyhfatWvH6448DEBMfT/uAAD55+WU+++EHfLy8APj+P//h8Tfe4JOXX8bB3h6DwYDWmr7du+Pq7IzWmvFz5vDpq6/y6Guv8eYTTxQdm5eXx/m4ODoEBvLvuXNRSpGXn4+jgwMnzp3D3s6OtLQ0Vq1axYqtW/GpXx97Fxc69ehhcVCIi4tj3759pKenExAQQGhoKA0bNrQoTXE1pUtZiMOqJ1AqAPij8I5BKdUfWKC1Hm16/CyA1voNpVQL4AWtdZlzICmlZgOzAXx9fXstW7asWvNfVRkZGbib5m8RV5PyqVjJMvrzyBHSMjIYZaqqqchvW7ZwJjYWJwcHgtq148CpU8yZOJGo+Hh+2bSJ+m5uzBg3jkOnT7Np/37slMLezo7po0ez8/BhjkdFobWmmbc3k4cN40JyMr9u3lzUAD2id2/at2zJ1gMH2HvyJHZK4ejgwH3jx7P76FGi4uJo6urK5exsjsTH89jUqVzJzmbxH38wZ+JEXJycrqlcCgoK2LlzJwBt2rShcePG0tOoDBX9nw0dOnSP1jq4rP21ERgmAWO01veZHt8F9NVaP6SUeglYpbXeXpm0g4ODdURERDXl/Nps3LiRkJCQ2s5GnSXlUzFbLaPIyEjCwsJITk6mXbt2jBkzxuKrea01J06coL2peioqKoqTJ08yYsQIK+X6+lTRZ0gpVW5gqI3G59JCvAbQWs8vZd/VCci020LUGRkZGaxevZpDhw7h6enJ1KlTaW9au9kSCQkJhIWFER0dzZQpU+jYsSMtWrTg7NmzVsi1KE9tBIYYwHzFbX+gSjNEaZl2W4haZzAY2L17Nxs2bCA/P59BgwYxaNCgovWWr1VOTg4bNmxg165duLq6Mn78eDqYJq0TNaM2AsNuoK1SqhUQC0zF2OAshLAR0dHRhIeHEx8fX7TecqNGjayS9pIlS4iKiqJXr14MHz687qy3fAOp7u6qS4EQwFspFQPM11ovUko9BKzC2F11sdb6SBXTlaokIWrBlStXWLt2Lfv27cPDw4NJkybRyQrTTSQnJ9OgQQMcHR0ZNmwYDg4O+Pn5WSnXoqqqNTBoraeVsT0cCLcgXalKEqIGaa3Zu3cv69atIzs7m/79+zNkyBCcnZ0tSjc3N5ctW7awfft2Bg0aREhICC1btrRSrsW1qjMjn6tC7hiEqDkXLlwgLCyM2NhYWrZsSWhoqMWDyQp7G61cuZK0tDS6d+9Ob9MIaFH7bDIwyB2DENUvOzub9evXExERQb169bj11lvp1q2bVcYObNiwgS1bttC4cWNmzpwpdwl1jE0GBiFE9dFac/DgQdasWcOVK1cIDg5m2LBhuJim3bhW+fn55OXl4erqSufOnXF1daVPnz5XzcAqap9NBgapShKieiQmJhIeHs758+fx8/Nj+vTpNDVN+meJ06dPEx4ejr+/P7fddhu+vr74+vpaIceiOthkYJCqJCGsKycnh02bNrFz505cXFy4+eab6dmzp8XVRoVzJh07doxGjRoRFBRknQyLamWTgUEIYR1aa44ePcqqVatIT0+nR48ejBgxgnr16lmc9smTJ/nxxx/RWjNs2DD69++Pg4N85dgCm3yXpCpJCMulpKQQHh7O2bNnadKkCZMnT6Z58+YVH1iB/Px8HBwcaNasGe3bt2f48OF4enpanmFRY2wyMEhVkhDXLi8vr2jsgIODA2PGjKF3797YWbj8ZXp6OmvWrCEtLY2ZM2fi7u7OxIkTrZRrUZNsMjAIIa5N4diBS5cu0a1bN0aOHGnxNOgGg4Fdu3axceNG8vPzGTBgAAaDQXob2TAJDELcAFJTU1m5ciUnT57Ex8eHGTNmEBAQYJV0v/vuOxISEqw+Z5KoPRIYhLiO5efns337drZs2YJSihEjRtCvXz+Lr+a11iilcHd3x9nZmcmTJ9OxY0dZOOc6YZOBQRqfhajYmTNnCA8P5+LFi3Tq1Mlq6y3v3buXvXv3MnPmTBwdHbnnnnuslGNRV9hkYJDGZyHKdvnyZVatWsXRo0fx8vJi+vTpWOMiKi4ujvDw8KI5k7Kzsy1ee0HUTTYZGIQQVysoKODPP/9k48aNaK0JCQlhwIABFo8dyM/PZ9WqVURERODm5sbf/vY3unbtKtVG1zEJDEJcB86fP09YWBhJSUlWW2+5kL29PYmJifTp04ehQ4daPGeSqPskMAhhwzIyMlizZg0HDx6kQYMGVl1ved26dYwfPx53d3dmzJhh8TgHYTtsMjBI47O40RkMBiIiIli/fj15eXkMHDiQwYMHW2W95Y0bN/Lnn3/i4uJCcnIy7u7uEhRuMDYZGKTxWdzIYmJiCAsLIz4+nsDAQMaOHYu3t7fF6R45cqRozqSePXsyfPhwq8yZJGyPTQYGIW5EV65cYd26dezduxcPDw8mTpxI586drdYIfPz4cdzc3JgyZQr+/v5WSVPYJgkMQtRxWmv27dvH2rVryc7Opl+/foSEhFi83nLhnEmdO3fG19eXm2++GUdHR6k2EhIYhKjLLly4QHh4ODExMbRo0YLQ0FCLF7gpud6ys7Mzvr6+Fgcacf2QwCBEHZSdnc2GDRvYvXu3VddbTk1NZcWKFZw6dQofHx9Zb1mUSgKDEHWI1pqEhAQ++OADMjMz6d27t1XWWy60b98+zp8/z8iRI+nbt6/MgCpKJYFBiDqi5HrLd9xxB82aNbM43TNnzmBvb09AQACDBg0iODjY4jmTxPXNJgODjGMQ15Pc3NyisQNOTk60bduWadOmWVxtZD5nUps2bQgICMDR0VHmNxIVssnAIOMYxPVAa82xY8dYtWoVly9fJigoiBEjRrB7926LgkLJOZOGDh3KTTfdZMWci+udTQYGIWxdSkoKK1as4MyZM/j6+jJp0iSrrLcMcOzYMdasWWP1OZPEjaNSgUEpNRlYqbVOV0o9D/QEXtVa763W3AlxnTFfb9ne3t5q6y1nZGSQmJhIYGAgnTt3xs3NjVatWlkp1+JGU9k7hhe01j8opQYCo4F/AR8BfastZ0JcZ06ePMmKFSu4dOkSXbt2ZeTIkXh4eFiUpsFgYPfu3WzYsAEHBwcee+wxHBwcJCgIi1Q2MBSYfo8DPtJaL1dKLaieLAlxfbl06RIrV67kxIkTeHt7c/fdd1vli9t8zqTC9ZYtXXtBCKh8YIhVSn0CjADeUko5AzJuXohyVNd6ywDJycksWrQIDw8PWW9ZWF1lA8MUYAzwL631JaVUU+Cp6suWELbt7NmzhIeHk5KSQseOHRk9ejQNGjSwKE2tNXFxcfj5+eHt7c2tt95Khw4dZCoLYXWVCgxa6ytKqURgIHAKyDf9FkKYuXz5MqtXr+bIkSM0bNjQaustX7hwgbCwMC5cuMCDDz6Il5cX3bt3t0KOhbhaZXslzQeCgfbA54Aj8A0woPqyJoTtKBw7sGnTJgoKCqy23nJ2djbr168nIiKCevXqMX78eOl+KqpdZT+1fwN6AHsBtNZxSinLulOUoJSyA14B6gMRWusvrZm+ENXl/PnzhIeHk5iYSNu2bRkzZgxeXl4Wp5uXl8dHH31Eeno6wcHBVp0zSYjyVDYw5GqttVJKAyil3CpzkFJqMXAzkKi17mK2fQzwPmAPfKa1fhOYAPgBF4GYyr8EIWpHRkYGa9eu5cCBAzRo0IDbb7+d9u3bW9wInJ2dDYCjoyODBw+mWbNmNG3a1BpZFqJSKhsYvjf1SvJUSv0duBf4tBLHfQF8AHxVuEEpZQ/8DxiJMQDsVkr9hrGaaofW+hOl1I/Aukq/CiFqkMFgYM+ePaxbt65oveVBgwbh5ORkUbqFcybt2rWLtm3b0rp1a3r16mWlXAtReUprXbknKjUSGAUoYJXWek0ljwsA/ii8Y1BK9QcWaK1Hmx4/a3pqNMY7k++VUt9prW8vI73ZwGwAX1/fXsuWLatU/mtKRkYG7u7utZ2NOsvWy+fy5cucOnWKjIwMPD09adu2rcXrImutSU5O5vTp0+Tm5tKoUSPat28vk92VwdY/QzWhojIaOnToHq11cFn7K90yZgoElQoGFfDDGAQKxWAcQf0+8F+l1CBgczn5WAgsBAgODtYhISFWyJL1bNy4kbqWp7rEVsuncL3lffv24e7ubtX1ln/66SeOHj1KkyZNCA0N5cyZMzZZRjXFVj9DNcnSMqpsr6R0oOStRRoQATyhtT5bhXOW9p+ktdZXgFmVzI9Muy1qRHWut2xvb4+dnR1t2rTB39+/aM6kM2fOWCn3Qlybyt4xvAvEAUswfrFPBZoAJ4DFQEgVzhkDmE8j6W9Ku9Jk2m1RE+Lj4wkLCyMmJobmzZszbtw4i9dbBorWWx4wYADBwcEyHkHUOZUNDGO01uYT5i1USu3UWr+slHquiufcDbRVSrUCYjEGmTuqkoDcMYjqZL7esqurKxMmTKB79+5WWW955cqVnDx5Eh8fH3x8fKyUYyGsq7KBwaCUmgL8aHo8yWxfma3XSqmlGO8mvJVSMcB8rfUipdRDwCqM3VUXa62PVCXTcscgqoPWmkOHDrFmzRoyMjKKxg64urpanPaePXtYuXKl1edMEqI6VDYwTMfYOPwhxkCwE7hTKeUKPFTWQVrraWVsDwfCq5ZVIapPUlIS4eHhREZG0qxZM6ZOnYqfn5/F6WqtUUrh6elJu3btGD16tKy3LOq8ys6VdBa4pYzdW62XncqRqiRhLbm5uWzatImdO3fi5OTEuHHj6Nmzp8UL5xSut9ywYUNGjBhB69atad26tZVyLUT1KjcwKKX+SzlVRVrrR6yeo0qQqiRhqbLWW3Zzq9Sg/jKVXG95yJAhVsqxEDWnojuGCNPvAUAn4DvT48nAnurKlBDV6eLFi6xYsYLTp0/j6+vLxIkTadGihcXpXrhwgV9++YWkpCRZb1nYtHIDQ+FEdkqpmcBQrXWe6fHHwOpqz10ZpCpJXIu8vDy2bt3Ktm3bsLe3Z/To0fTp08fiaqNCDg4OFBQUMHXqVNq3b2+VNIWoDZVtfG4GeGCc4A7A3bStVkhVkqiqkydPsnLlSlJTU+nSpQujRo2yynrLERERJCQkcMstt+Dj48ODDz5otUAjRG2pbGB4E9irlNpoejwEWFAdGRLCmmpiveXAwEDy8/NxcHCQoCCuC5UNDF8ABcBjGAPCixhHPtcKqUoSFSkoKGD79u1s3rwZpRTDhw+nf//+Fo8dyMrKYu3atezduxcPDw+rzpkkRF1R2cDwIWAAXLXWvymlGgI/Ab2rLWflkKokUR7z9ZY7dOjAmDFjLF5vuZDBYOD48eNWmzNJiLqosoGhr9a6p1JqH4DWOlUpZdnk80JYWXp6OqtXr+bw4cM0bNiQO+64g7Zt21qcbnx8PHv27CE0NBQ3NzceeeQRCQjiulbZwJBnWmCncAU3H4x3EELUOoPBUDR2oKCggCFDhjBgwACL1zMoOWdSv379aNSokQQFcd2rbGD4P+AXoLFS6jWMcyU9X225EqKSoqKiCAsLIzExkTZt2jB27FiL11sunDNp9erVZGZm0rt3b1lvWdxQKjslxrdKqT3AcIzTbt+qtT5WrTkrhzQ+i8zMTNasWcOBAweoX78+U6ZMoUOHDlZpBC4oKGDjxo14enpyxx130KxZrfXMFqJWVGUFt+PA8WrMS6VJ4/ONq3C95fXr15Obm8uAAQMYPHiwVdZb3rlzJ/3798fR0ZEZM2ZQv359ywNNUhJERkJAAMg028JGVDowCFHbYmNjCQ8PJy4ujoCAAEJDQy1e06DknEne3t506tTJOr2Yli6FWbPAyQlyc2HRIphW6oTDQtQpEhhEnZeVlcW6devYs2cP7u7u3HbbbXTp0sXiq/mUlBRWrFjBmTNn8PX1ZdKkSTRv3rziAysjKckYFLKyjD9gfDxihNw5iDpPAoOos7TW7N+/n7Vr15KVlUXfvn0JCQmxWiNweHg4MTExVp8zCTBWHzk5/RUUABwdjdslMIg6ziYDgzQ+X//i4+MJDw8nOjqa5s2bExoaSpMmlg+2P3nyJE2bNsXDw4Nx48bh6Oho8ZxJpQoIMFYfmcvLM24Xoo6zycAgjc/Xr5ycHDZs2MCuXbtwdXVl/PjxBAUFVbnaKDk1lS9/+YUn7r2X3YcOYQecOnKEEydO0L9/f0aNGmVxt9Zy+fgY2xRmzTLeKeTlGR/L3YKwATYZGMT1R2vN4cOHWb16NRkZGfTq1Yvhw4df83rL3g0b8sS995Kfn8/P4eGkxMcT4ONTtN5ydSooKDDOyTRtmrFNQXolCRsjgUHUOvP1lps2bXrN6y2//9VX7DlyBCdHR8YMGsSabdsI7d2bTXv24OriQq6zM3P79WPdzp0s/eMPNNA/KIj7b7+d3YcO8d5XX+Hq7Eyzxo15+ZFHOBUZyb8WL8agNZ4eHrzy6KO4ODuz9I8/WL1tGwUGA7eOGMFtI0eyfN06tuzZQ35+Pr06d+auCROMmfLxkYAgbI4EBlFrcnNz2bx5Mzt27MDJyYnQ0FB69ep1TY3AWyIiuJCUxJdvvkl6ejrnY2NZs20bw4cO5XBUFEGdOzMuJITLGRl8vXw5i19/HUcHBx5/4w1ORUayfudOHrzjDm7q0QODwTjbyxsLF/La44/T1MeHb3//nV/XrqVPt25s27ePxa+/jsFg4J7nnmNY374AXMnK4n8vvigzrQqbJ4FB1DitNcePH2fVqlWkpaXRvXt3Ro4cadF6y6ejoujVuTM7d+5k48aNuHt6AuDu7l6sLSHqwgUuJCXxwPz5AKRnZnIhKYkZt97K57/8wu8bNtCna1f+NnIkZ6Kjef699wDIzcujb/funI6K4mx0NPc9b5wRJjMri/jkZAC6tW8vQUFcFyQwiBqVlZXFkiVLOH36NI0bN+aee+6xynrL9V1cWLp8OV2bNKFt27Z069mTw0uWAODo4EB+QQEA/r6+NG/ShE9efhkHe3sMBgNaa3Lz83l29my01oyfM4eRAwbQpkUL3nziCXxMgSUvL4/zcXF0CAzk33PnopQiLz8fRwcHTpw7h70s0iOuExIYRI3Iz89n69at7N69G0dHR0aNGkWfPn0sXjgH4NixYxzevRsXBwcOp6QQk5eHU6NGRfv7BQXxzqJFbImI4O2nnmL6Lbcw+4UXsLOzw8Henlcfe4xf1qxhx/79GLSmX/fuuNerx7OzZ/Pi//1fUVC5d+JE+gcF0bdbN2bNm4ednR3OTk68P2+exa9BiLpEaa1rOw9VZjaO4e+nTp2q7ewUs3HjRkJCQmo7G3XKqVOnWLFiBampqfj4+HDnnXdSv359i9I0GAxcvnwZT09P8vLy2LlzJ3379rV4zqS6QD5D5ZPyqVhFZaSU2qO1Di5rv03eMcg4BtuQlpbGypUrOX78OI0aNeKuu+4iKirK4qAQGxtLWFgYWVlZzJkzB0dHRwYNGmSlXAshbDIwiLqtoKCAHTt2sHnzZrTWDBs2jP79++Pg4EBUVNQ1p1tyzqTRo0fj4CAfYSGsTf6rhFWdO3eO8PBwkpOTad++PWPGjMHT1EPIEhcvXmTRokVkZWXJestCVDMJDMIqzNdb9vT0ZNq0abRr187idLOzs3FxcaFhw4Z06dKFnj174uvra4UcCyHKIoFBWMRgMLBr1y42bNhAQUEBgwcPZuDAgVZbb/nQoUPMmTMHd3d3xo4da6VcCyHKI4FBXLOoqCjCw8NJSEigdevWjB07lkZm3USvReF6y2vWrCEjI4Pg4GBpRxCihsl/nKiyzMxM1q5dy/79+6lfvz6TJ0+mY8eOFo/6zc/P59tvvyUyMpJmzZoxbdo0WW9ZiFoggUFUmsFgYO/evaxbt47c3FxuuukmhgwZYvHYAYPBYBxs5uCAj48PnTt3pmfPntZdOEcIUWkSGESlxMXFERYWRlxcHC1btmTcuHFWWW/5+PHjrF69mqlTp+Lr60toaKiVciyEuFZ1JjAopUKAV4AjwDKt9cbazI8wysrKYv369URERODm5sbf/vY3unbtanG10cWLF1mxYgWnT5/G19e3aEZTIUTtq9bAoJRaDNwMJGqtu5htHwO8D9gDn2mt3wQ0kAG4ADHVmS9RMa01Bw4cYM2aNWRlZdGnTx+GDh1qlfWWt2zZwqZNm7C3t6+e9ZaFEBap7juGL4APgK8KNyil7IH/ASMxBoDdSqnfgC1a601KKV/gXWB6NedNlCEhIYHw8HCioqLw9/dn3LhxVllvuVBeXh4dO3Zk1KhR1bPeshDCItU+iZ5SKgD4o/COQSnVH1igtR5tevwsgNb6DdNjJ2CJ1npSGenNBmYD+Pr69lq2bFm15r+qMjIycHd3r+1sXJP8/HwiIyOJjY3FwcGBwMBAmjRpYnG1UXZ2NmfOnKFJkyY4Ozvj5uYm6xaUw5Y/QzVByqdiFZXR0KFD69wken5AtNnjGKCvUuo2YDTgifEuo1Ra64XAQoDg4GBd12ZZtMWZH7XWHDlyhFWrVpGRkUHPnj0ZPnw49erVsyjdwjmT9u7dC0D//v25fPmyzZVPTbPFz1BNkvKpmKVlVBuBobRLRa21/hn4uVIJ/DXttlUzdiNKTk4mPDycc+fO0bRpU26//Xb8/f0tTvf8+fP88ccfJCcn06FDB8aMGUODBg3YuHGj5ZkWQlSr2ggMMUBzs8f+QFxVEpBpty2Xm5vLli1b2L59O46OjowdO5bg4GCrNQJfvHiRgoIC7rjjDtq2bWuVNIUQNaM2uoLsBtoqpVqZ2hOmAr9VJQGl1C1KqYVpaWnVksHKys3LY9o//0lcYiIAOw8cYPHvvwPw9mefcbGW81eawrEDH374IVu3bqVr16489NBDFvcMMhgM7Ny5k3379gEQFBTEnDlzJCgIYYOqNTAopZYCO4D2SqkYpdQsrXU+8BCwCjgGfK+1PlKVdLXWv2utZzdo0MCq+U3JyOFA9CVSMnIq9Xx7Ozv+ec89vLlwIdk5Obz35ZfcZqrXe/q++/Cycv4slZqaytKlS/nuu+9wdnZm5syZ3HrrrRY35EVFRbFw4UJWrVrF2bNnAVBKyRxHQtioav3P1VpPK2N7OBBeneeuquX7Y5n700Ec7ezIMxh4e2I3BgV6MPeddygwGMgvKODlhx/msx9/xNnJiYSUFO6eMIHeXbvy+4YN/GPBAiYMG4an6Ut21rx5vP7443zz22/06tKFkD59yM7J4e65c/nuP/+p0V45+fn5bNu2ja1bt2JnZ8fIkSPp27evxestl5wzacqUKXTo0MFKuRZC1BabvKSzduNzSkYOc386SHaegWyMI3Cf/ukgm54YzP9efBFHR0e27tnD4p+NbeNNfXx4/h//KDp+YK9ePPfuu/zn2WfZb+qBU+iWYcP4ZNkyQvr0YeOuXYT06VOjQeH06dOsWLGCixcv0rlzZ0aNGmXx0pqFEhMTOXjwIAMGDGDw4MHXxXrLQggbDQzWbnyOSc3C0c6uKCgAONrZcTIuhd/+WEbKpUvk5edTz9WVxl5edDe7Ks7KyeGzH37goTvv5INvv2Vgx47F0m4XEEBCSgpp6emEbdrEU7NmWSPLFUpLS2PVqlUcO3aMRo0aceedd9K6dWuL042LiyM2NpbevXvTqlUrHn30UasFGiFE3WCTgcHa/Bu6kldirp48g4GjRyLoEBjIrEmT2BIRwTe/GdvI7c0aaT9aupSJo0Zxe2go98+fT2Qpy1iOHjiQpWFhXMnKokXTptX6WgoKCti5cyebNm26ar1lS5ivt1y/fn2CgoJwdHSUoCDEdcgmA4O1q5IauTvz9sRuPF2ijaGTVwHPvfsue48epXXz5lcdd+LcOQ4cP85jd98NwHOzZ/Pwyy9z55QpxZ4XOmQIY//+d56+7z6r5LcskZGRhIWFWXW95ZJzJvXt25eQkBCLV2gTQtRd1T4lRnUKDg7WERERVksvJSOHmNQs/Bu60sj92haar41Rmenp6axZs4ZDhw7h6enJ2LFjrbLeMhh7Mn3wwQf4+fkRGhpq8ZxJMmq1YlJG5ZPyqVhFZaSUqnNTYtRZjdydrzkg1AaDwcDu3bvZsGED+fn5VltvOScnh6NHj9KjRw8aNmzIrFmzaNq0qcxvJMQNwiYDg0yJAdHR0YSHhxMfH2/V9ZbN50zy8/OjcePGsrymEDcYm5wEv7oGuF2L0gbFxSYkMGj6dGbNm8cdTzxB+KZNpR4bm5DA/S++WKXzXblyheXLl7N48WIyMzOZPHky06dPLzUo3PLAA5VONzk5mQ8+/pjH33wTDw8P7rvvPho3blylNIQQ1webvGOoK0obFFfYR6dT69Z88vLLZFy5wuRHH2XUwIE4WDCgTGtdtN5yTk6Oxestm7eneLo68OWXX3IpIwNvb2/uu+8+WThHiBuYBIZrVNaguP8NLb7CmXu9eng3bMj4f/yD8IULAbj/xRd58cEHAUi/coVn332X87GxjAsJYfott7B83Tq27tlDgcFAZEwMMydMYPGPP5KSlsbIXr144P77SbtyhYdffRWD1nh6ePDKo4/i4uzMu198wf5jx2gXEEB+QUGpeTcGtAP426UTa/DgrYndmThxIvnAO4sXXxUUPlq6lBZNmzIuJIR9R4/y85o1vPLoo7zw/vu4ODsTl5hITm4uk8eM4Zc1a0hLT+f955+nsZcXz7//PhcSE8nMyuKBqVON51+3jm1791JQUEBkXBzz7r+fnp07W/PtEUJYwCYDQ11oYyhrUFxuQfHxEAkpKaSmpZXZcHshMZFPX3kFJ0dHpj/5JGMHDQKgwGDg9cce4z8LF/LvxYsZ17MnHbt1Y21EBI0bN+aZ557jtccfp6mPD9/+/ju/rl1LUMeOnD5/nq/eeovYhAR+Xbv2qvOlZOTwyk+7GKQi8be7zKb8Vjz900G2zR1GduYljp45w6x58ypdDq38/Zn3wAO8+tFH7D9+nI9feolvfvuNVVu3ctf48cy7/35cXVy4dPkys55/nodvuw0wTkD43nPPsf/4cb5ZvlwCgxB1iE0Ghrow7XZZg+Kc7I1X20fPnOG+559HKcULc+bw6kcfFT3PvItwgJ8fbq6uALRp2ZJY00ytnq6ufPDBB1xITCSweXMeefhhsnJz+dm0nsGZ6Gief+89wPgl27d7d87HxdHZFCz9fH3xKjGGIT8/n7XrNxBqfwgDij9z/TlX0BB3BztiUrPwdvqrCqzQLQ88UCyoleze3KFVKwB8GzXCx9TO4evtzclz5zAYDHy8bBkHTpzA3s6OC6bXBsbzADT19uZSenpFxS2EqEE2GRjqgrIGxdlfOgVc/QVr0JrcvDwKDAbOxcQUbY+MjeVKVhZOTk6cPn8eJzs7tm7dSkx8PCOCg+nety+b9+7FxcWFrNxcCr+W27RowZtPPIGPlxdgXEf5dFQUv61fD8CFpCQuXrpULM/ff/89p06dIkZ7sTPHnysY2yfyDAb8G7qSnVn6rLIN3N1JSEkBjAHPnHnQML8n0sDJyEhOnT/P56+/zqX09GIN2eUFGyFE7ZLAYIHxQX4MaONdbFDcxo2nSn3u1NBQ7nr6adq3akVjb++i7U0bN+blDz8kMjaWdn5+fLdkCenp6XRo3557772X/ceOlZres7Nn8+L//V9RO8K9EyfSPyiIQH9/7p47l9YtWuDj5UVaWhqurq44OTkxYMAA+vXrx+HLzmz/6SAeZgGtkbszsZmlv85RAwfy6GuvsffoUfx8fStdPi39/MjPz+e+55+nXatWeLi5VfpYIUTtkZHPVlbVUZlaa44ePcqqVatIT0+3+nrLmzdvpk+fPowYMaLYfmuM8r4WMmq1YlJG5ZPyqZiMfLZhycnJrFixgrNnz9KkSROmTJlilfWWz507R3h4eNGcScHBV7//tjbKWwhRc2wyMNSFXkmWyMvLY8uWLWzbts3q6y1v27aNtWvX4unpybRp06w2Z5IQ4sZhk4GhLvRKulYnTpxgxYoVpKWl0a1bN0aOHGnx0poGg4Hc3FxcXFxo164dubm5VpkzSQhxY7LJwGCLUlNTWblyJSdPnsTHx4eZM2fSsmVLi9ONjo4mLCwMb29vJk2ahI+PD0OHDrVCjoUQNyoJDNXMfL1lpVS1rbfcqVMnK+VYCHGjk8BQjc6cOUN4eDgXL16kU6dOjB492iornkVGRvLdd9+Rm5tr8ZxJQghRkgSGanD58mVWrVrF0aNH8fLystp6ywUFBdjb2+Pj40NAQADDhg3Dx8fHCjkWQoi/SGCwooKCAqKjo9m+fTtaa4YOHcpNN91klfWW169fT3x8PPfeey9ubm7cfvvtVsq1EEIUJ4HBSiIjIwkPDycpKYl27doxZswYGjZsaFGaJddb7t27N/n5+dLbSAhRrWwyMNSlcQwZGRmsWbOGgwcP4unpSefOnZk0aZLF6aanp/Pjjz8SFRWFv78/oaGhNG3a1Ao5FkKI8tlkYKgL4xgMBgMRERGsX7+e/Px8Bg0axKBBg9i2bZtF6WqtUUrh6uqKwWDglltuoUePHrLeshCixthkYKhtMTExhIWFER8fT2BgIKGhoVZZb/no0aPs3LmTu+66CycnJ+69914JCEKIGieBoQquXLnC2rVr2bdvHx4eHkyaNIlOnTpZ/OVdcs6kzMxMnJycJCgIIWqFBIZyFM5A6ufpQtSpo6xdu5b4ixeJvXKFetnZHPzmG1r5+/Pc/ffjeA09jwoKCti4cSPbt2+3+pxJQghxrSQwlMG4LvJBfOyyCFJn8VaZ+DZtyo7z5/m/55+nuakheN/RoxhKrORWWXZ2dsTExNClSxerzJkkhBDWIIGhFCkZOTz/0166E0MHlUgODuwoCOSelk0Z7uZWFBQAenTqhNaaVz78kDPR0Vy6dIlGzZrRtYxZTVNTU1m3bh2jR4/Gw8OD6dOnWzzOQQghrEm+kUrQWrNj9z5utj+EE3mcKPBhb54fzs4unIlNoJ2fcfW1i2lpPPX221xKT2f8sGHkFxTwxRtv8OPy5bz56ad8+847xdI1nzPJzs6Obt264eHhIUFBCFHnyLeSmcTERMLDwzl//jwZuLE9pw0p2rgcpZ3BQBv/JsQnxQPg1aABi157jRfef5+s7Gy6d+gAgHeDBlzOyCiW7unTp1mxYgUXL16kc+fOjBo1yipzJgkhRHWoU62cSik3pdQepdTNNXne3NxcVq9ezSeffEJiYiI333wzIyZMJdPBAw9nB1wc7Xh7YjfG3NSXTbt2ERMfX3RsgcGAvZ0dB44fByA5Le2qtY0PHz4MwJ133smkSZMkKAgh6rRqvWNQSi0GbgYStdZdzLaPAd4H7IHPtNZvmnbNBb6vzjyZ01pz7NgxVq1axeXLl+nRowcjRoygXr169AIGtvW5al3kVx59lNc/+YSc3FycnZxo4u3N5DFjeP/rr5n57LOkpqby0qOPsm3bNgIDA2natCljxozBwcFBqo2EEDahur+pvgA+AL4q3KCUsgf+B4wEYoDdSqnfgGbAUcClmvMEQEpKCitWrODMmTM0adKESZMm0bx582LPKW1d5A6BgXw4f/5V6c1/8EEAfv31V7Zv2EBSUhIDBw6kadOmuLjUyEsSQgirqNbAoLXerJQKKLG5D3Baa30WQCm1DJgAuANuQCcgSykVrrW+tn6gFYiIiGDlypU4ODgwZswYevfubfHYgYyMDFavXs2hQ4fw9PRk6tSptG/f3ko5FkKImqO01tV7AmNg+KOwKkkpNQkYo7W+z/T4LqCv1voh0+OZQLLW+o8y0psNzAbw9fXttWzZsirnKTU1lfj4eFq3bm21BW7Onz/P+fPn8fX1pU2bNhav0Ha9ysjIkPEaFZAyKp+UT8UqKqOhQ4fu0VoHl7W/Niq9S5vnoSg6aa2/KO9grfVCYCFAcHCwDgkJsWbeqiQmJoa8vDxatWpFfn4+aWlpHDp0iNrMU123ceNGKZ8KSBmVT8qnYpaWUW0EhhjAvDLfH4irSgK1Pe22+ZxJzZs3p1WrVjg4OFg8kZ4QQtQFtREYdgNtlVKtgFhgKnBHVRKorWm3tdbs3buXdevWkZOTQ//+/RkyZEhNZkEIIapddXdXXQqEAN5KqRhgvtZ6kVLqIWAVxu6qi7XWR6qYbq3cMZw4cYI//viDli1bEhoaSuPGjWv0/EIIUROqu1fStDK2hwPhFqRbY3cM2dnZxMfHExAQQPv27Zk2bRpt27aVKbGFENctGXFVBq01Bw8eZM2aNRQUFPD444/j5OREuzImxxNCiOuFTQaG6q5KMp8zyc/Pj3HjxlmtW6sQQtR1NhkYqrMq6dKlS3zyySc4OzvLestCiBuSTQYGa9Nak5CQQJMmTfD09OTmm2+mffv21KtXr7azJoQQNa5Oza5aWUqpW5RSC9PS0ixOKyUlhW+++YaFCxeSmJgIQI8ePSQoCCFuWDZ5x2CNqqS8vDy2bNnC9u3bi+ZM8vb2tmIuhRDCNtlkYLBUQUEBCxcuJDk5mW7dusl6y0IIYcYmA4OlvZLs7e3p27cv3t7eBAQEWDVvQghh62yyjUFr/bvWenaDBg2uOY3g4GAJCkIIUQqbDAxCCCGqjwQGIYQQxdhkYLBmd1UhhBDF2WRgsEYbgxBCiNLZZGAQQghRfSQwCCGEKEYCgxBCiGIkMAghhCjGpkc+A5eVUqfKeWoDoKyuS2XtK217yW3lPfYGksvJ07Uq77VYckxVy0jKp+J9VS2jkvvqShnVlfIp+biulE9lj6mO/7GS26r6GWpZzj7jlNPX6w+wsKr7Stteclt5j4GImn4tlhxT1TKS8rH+Z6iUfXWijOpK+dxon6Fr2Wbtz9D1XpX0+zXsK217yW0VPa4O13KOyhxT1TKS8ql4X1XLqCbK51rOU1fKp7J5sVRd+Qxdyzarlo8yRRdhJUqpCK11cG3no66S8qmYlFH5pHwqZmkZXe93DLVhYW1noI6T8qmYlFH5pHwqZlEZyR2DEEKIYuSOQQghRDESGIQQQhQjgUEIIUQxEhhqgFLKTSm1Ryl1c23npa5RSoUopbYopT5WSoXUdn7qGqWUnVLqNaXUf5VSM2o7P3WRUmqQ6fPzmVJqe23np65RSrVQSv2mlFqslHqmMsdIYLgGpgJOVEodLrF9jFLqhFLqdIk3YC7wfc3msvZUsXw0kAG4ADE1ndfaUMXymQD4AXncIOUDVSsjrfUWrfUDwB/Al7WR35pWxc9QOyBMa30v0KlSJ6iOEYTX+w8wGOgJHDbbZg+cAQIBJ+CA6U0YAUwFZgI313be62D52Jn2+wLf1nbe62D5PAPcb3rOj7Wd97pYRmb7vwfq13be61r5AI2ADcB64J7KpG+TcyXVNq31ZqVUQInNfYDTWuuzAEqpZRiv9twBN4xvUJZSKlxrbajJ/Na0qpSP1vqoaX8q4Fxzuaw9Vfz8RAO5pucU1Fgma1kVy+ioUqoFkKa1vlyzOa0dVSyfPGC+6Zgfgc8rSl8Cg/X4YfwnLhQD9NVaPwSglJoJJF/vQaEcpZaPUuo2YDTgCXxQC/mqK0otH+B94L9KqUHA5trIWB1SVhkBzKISX3jXubLK52NggVLqDiCyMglJYLAeVcq2otGDWusvai4rdVKp5aO1/hn4uaYzUweVVT5XMH7piXL+x7TW82s4L3VRWZ+hw8CkqiQkjc/WEwM0N3vsD8TVUl7qIimf8kn5VEzKqHxWKx8JDNazG2irlGqllHLC2OD8Wy3nqS6R8imflE/FpIzKZ7XykcBwDZRSS4EdQHulVIxSapbWOh94CFgFHAO+11ofqc181hYpn/JJ+VRMyqh81V0+MomeEEKIYuSOQQghRDESGIQQQhQjgUEIIUQxEhiEEEIUI4FBCCFEMRIYhBBCFCOBQQgrUUo9V+KxrA0gbJKMYxDCQkophXGemstaa/fazo8QlpI7BiFMlFJvKaXmmD1eoJR6Qin1lFJqt1LqoFLqJdO+AKXUMaXUh8BeYBHgqpTar5T61vScDLO0nlZKHVJKHVBKvWna1loptdK0ut8WpVQH0/bJSqnDpufe6DOqilogdwxCmCilegDvaa2HmB4fBd4EBgL3Y7wr+A14G4gCzgI3aa13mp6fYX7HUPhYKTUWeAEYobW+opTy0lpfVEqtAx7QWp9SSvUF3tBaD1NKHQLGaK1jlVKeWutLNVUGQoBMuy1EEa31PqVUY6VUM8AH4+JB3YBRwD7T09yBthgDw/nCoFCBEcDnpim0MQUFd+Am4AdjTRTw10JF24AvlFLfI1OSi1oggUGI4n7EOHd9E2AZEIDxSv4T8yeZVs/KrGSaCrO1OUzsgEta66CST9ZaP2C6gxgH7FdKBWmtU6rwGoSwiLQxCFHcMozTFU/CGCRWAfearvBRSvkppRqXcWyeUsqxlO2rTWnUM6XhZVqC8pxSarJpm1JKdTf93Vpr/afW+kUgmeJz7AtR7eSOQQgzWusjSikPIFZrfQG4oJTqCOwwVflkAHdS+vrLC4GDSqm9WuvpZmmuVEoFARFKqVwgHHgOmA58pJR6HnDEGJQOAO8opdpivNNYZ9omRI2RxmchhBDFSFWSEEKIYiQwCCGEKEYCgxBCiGIkMAghhChGAoMQQohiJDAIIYQoRgKDEEKIYiQwCCGEKOb/AfLkJzYGFGTWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee=np.geomspace(5000,80000000,num=10)\n",
    "ax=table_fer[table_fer['vertices'] < 100000].plot.scatter(x='vertices',y='edges',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,label='small',\n",
    "                          title='Graphs from FERRARI paper, Table 1(a)(b)')\n",
    "ax=table_fer[table_fer['vertices'] >= 100000].plot.scatter(ax=ax,x='vertices',y='edges',\n",
    "                          logx=True,logy=True,\n",
    "                          grid=True,c='red',label='large',\n",
    "                          title='Graphs from FERRARI paper, Table 1(a)(b)')\n",
    "ax.plot(ee,ee,'--',ee,2*ee,'-',label='dense vs sparse',c='gray')\n",
    "for row in table_fer[table_fer['vertices'] < 100000].itertuples():\n",
    "    ax.annotate(row[0], xy=(row.vertices,row.edges),\n",
    "                xytext=(3,-3), textcoords='offset points',horizontalalignment='left',\n",
    "                family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "    #print(row)\n",
    "for row in table_fer[table_fer['vertices'] >= 100000].itertuples():\n",
    "    ax.annotate(row[0], xy=(row.vertices,row.edges),\n",
    "                xytext=(-3,-8), textcoords='offset points',horizontalalignment='right',\n",
    "                family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets from PReaCH (`table_rch`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"PReaCH: A Fast Lightweight Reachability Index using Pruning and Contraction Hierarchies\" (2014)  \n",
    "https://arxiv.org/abs/1404.4465"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Table 1: Headers](images/preach/PReaCH_arXiv-table_1-datasets_used-header.png)  \n",
    "![Table 1: Instances used for our experiments. _d_ is the maximal path length.](images/preach/PReaCH_arXiv-table_1-datasets_used-real_graphs-no_caption.png)\n",
    "\n",
    "- $m/n$ - edge density, number of edges divided by number of nodes (vertices)\n",
    "- $d$ - length of the longest path, or maximal path length\n",
    "- % pos - the fraction of positive queries in a random sample of 100000 queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['small, dense' 'small, sparse' 'large' 'stanford']\n"
     ]
    },
    {
     "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>type</th>\n",
       "      <th>vertices</th>\n",
       "      <th>edges</th>\n",
       "      <th>edges/vertices</th>\n",
       "      <th>maxlevel</th>\n",
       "      <th>r-score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graph</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>arXiv</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6000</td>\n",
       "      <td>66707</td>\n",
       "      <td>11.12</td>\n",
       "      <td>167</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseer-sub</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>11000</td>\n",
       "      <td>44000</td>\n",
       "      <td>4.13</td>\n",
       "      <td>36</td>\n",
       "      <td>0.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6793</td>\n",
       "      <td>13361</td>\n",
       "      <td>1.97</td>\n",
       "      <td>16</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pubmed</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>9000</td>\n",
       "      <td>40028</td>\n",
       "      <td>4.45</td>\n",
       "      <td>19</td>\n",
       "      <td>0.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yago</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6642</td>\n",
       "      <td>42392</td>\n",
       "      <td>6.38</td>\n",
       "      <td>13</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agrocyc</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>13000</td>\n",
       "      <td>14000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>16</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amaze</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>3710</td>\n",
       "      <td>3947</td>\n",
       "      <td>1.06</td>\n",
       "      <td>16</td>\n",
       "      <td>0.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anthra</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>12000</td>\n",
       "      <td>13000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>16</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecoo</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>13000</td>\n",
       "      <td>14000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>22</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>38811</td>\n",
       "      <td>39816</td>\n",
       "      <td>1.01</td>\n",
       "      <td>18</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kegg</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>3617</td>\n",
       "      <td>4395</td>\n",
       "      <td>1.22</td>\n",
       "      <td>26</td>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtbrv</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>9602</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>22</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nasa</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>5605</td>\n",
       "      <td>6538</td>\n",
       "      <td>1.17</td>\n",
       "      <td>35</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vchocyc</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>9491</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>21</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xmark</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>6080</td>\n",
       "      <td>7051</td>\n",
       "      <td>1.16</td>\n",
       "      <td>38</td>\n",
       "      <td>0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseer</th>\n",
       "      <td>large</td>\n",
       "      <td>693947</td>\n",
       "      <td>312383</td>\n",
       "      <td>0.45</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseerx</th>\n",
       "      <td>large</td>\n",
       "      <td>6540401</td>\n",
       "      <td>15011260</td>\n",
       "      <td>2.30</td>\n",
       "      <td>59</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cit-patents</th>\n",
       "      <td>large</td>\n",
       "      <td>3774768</td>\n",
       "      <td>16518047</td>\n",
       "      <td>4.38</td>\n",
       "      <td>32</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Go-uniprot</th>\n",
       "      <td>large</td>\n",
       "      <td>6967956</td>\n",
       "      <td>34770235</td>\n",
       "      <td>4.99</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot22m</th>\n",
       "      <td>large</td>\n",
       "      <td>1595444</td>\n",
       "      <td>1595442</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot100m</th>\n",
       "      <td>large</td>\n",
       "      <td>16087295</td>\n",
       "      <td>16087293</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniprot150m</th>\n",
       "      <td>large</td>\n",
       "      <td>25037600</td>\n",
       "      <td>25037598</td>\n",
       "      <td>1.00</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email-EuAll</th>\n",
       "      <td>stanford</td>\n",
       "      <td>231000</td>\n",
       "      <td>223000</td>\n",
       "      <td>0.97</td>\n",
       "      <td>7</td>\n",
       "      <td>0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2p-Gnutella31</th>\n",
       "      <td>stanford</td>\n",
       "      <td>48000</td>\n",
       "      <td>55000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>14</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soc-LiveJournal1</th>\n",
       "      <td>stanford</td>\n",
       "      <td>971000</td>\n",
       "      <td>1024000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>24</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>web-Google</th>\n",
       "      <td>stanford</td>\n",
       "      <td>372000</td>\n",
       "      <td>518000</td>\n",
       "      <td>1.39</td>\n",
       "      <td>34</td>\n",
       "      <td>0.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wiki-Talk</th>\n",
       "      <td>stanford</td>\n",
       "      <td>2282000</td>\n",
       "      <td>2312000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>8</td>\n",
       "      <td>0.008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           type  vertices     edges  edges/vertices  maxlevel  \\\n",
       "Graph                                                                           \n",
       "arXiv              small, dense      6000     66707           11.12       167   \n",
       "citeseer-sub       small, dense     11000     44000            4.13        36   \n",
       "Go                 small, dense      6793     13361            1.97        16   \n",
       "Pubmed             small, dense      9000     40028            4.45        19   \n",
       "Yago               small, dense      6642     42392            6.38        13   \n",
       "agrocyc           small, sparse     13000     14000            1.07        16   \n",
       "amaze             small, sparse      3710      3947            1.06        16   \n",
       "anthra            small, sparse     12000     13000            1.07        16   \n",
       "ecoo              small, sparse     13000     14000            1.08        22   \n",
       "Human             small, sparse     38811     39816            1.01        18   \n",
       "kegg              small, sparse      3617      4395            1.22        26   \n",
       "mtbrv             small, sparse      9602     10000            1.09        22   \n",
       "nasa              small, sparse      5605      6538            1.17        35   \n",
       "vchocyc           small, sparse      9491     10000            1.09        21   \n",
       "xmark             small, sparse      6080      7051            1.16        38   \n",
       "citeseer                  large    693947    312383            0.45        13   \n",
       "citeseerx                 large   6540401  15011260            2.30        59   \n",
       "Cit-patents               large   3774768  16518047            4.38        32   \n",
       "Go-uniprot                large   6967956  34770235            4.99        21   \n",
       "Uniprot22m                large   1595444   1595442            1.00         4   \n",
       "Uniprot100m               large  16087295  16087293            1.00         9   \n",
       "Uniprot150m               large  25037600  25037598            1.00        10   \n",
       "email-EuAll            stanford    231000    223000            0.97         7   \n",
       "p2p-Gnutella31         stanford     48000     55000            1.15        14   \n",
       "soc-LiveJournal1       stanford    971000   1024000            1.05        24   \n",
       "web-Google             stanford    372000    518000            1.39        34   \n",
       "wiki-Talk              stanford   2282000   2312000            1.01         8   \n",
       "\n",
       "                  r-score  \n",
       "Graph                      \n",
       "arXiv               0.150  \n",
       "citeseer-sub        0.004  \n",
       "Go                  0.002  \n",
       "Pubmed              0.007  \n",
       "Yago                0.002  \n",
       "agrocyc             0.001  \n",
       "amaze               0.170  \n",
       "anthra              0.001  \n",
       "ecoo                0.001  \n",
       "Human               0.000  \n",
       "kegg                0.200  \n",
       "mtbrv               0.002  \n",
       "nasa                0.006  \n",
       "vchocyc             0.001  \n",
       "xmark               0.014  \n",
       "citeseer            0.000  \n",
       "citeseerx           0.002  \n",
       "Cit-patents         0.001  \n",
       "Go-uniprot          0.000  \n",
       "Uniprot22m          0.000  \n",
       "Uniprot100m         0.000  \n",
       "Uniprot150m         0.000  \n",
       "email-EuAll         0.050  \n",
       "p2p-Gnutella31      0.008  \n",
       "soc-LiveJournal1    0.210  \n",
       "web-Google          0.150  \n",
       "wiki-Talk           0.008  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Table 1: Instances used for experimets from PReaCH (table_rch) { form-width: \"25%\" }\n",
    "\n",
    "csv=u\"\"\"Graph;type;vertices;edges;edges/vertices;maxlevel;r-score\n",
    "arXiv;small, dense;6000;66707;11.12;167;0.15\n",
    "citeseer-sub;small, dense;11000;44000;4.13;36;0.004\n",
    "Go;small, dense;6793;13361;1.97;16;0.002\n",
    "Pubmed;small, dense;9000;40028;4.45;19;0.007\n",
    "Yago;small, dense;6642;42392;6.38;13;0.002\n",
    "agrocyc;small, sparse;13000;14000;1.07;16;0.001\n",
    "amaze;small, sparse;3710;3947;1.06;16;0.17\n",
    "anthra;small, sparse;12000;13000;1.07;16;0.001\n",
    "ecoo;small, sparse;13000;14000;1.08;22;0.001\n",
    "Human;small, sparse;38811;39816;1.01;18;0.000\n",
    "kegg;small, sparse;3617;4395;1.22;26;0.20\n",
    "mtbrv;small, sparse;9602;10000;1.09;22;0.002\n",
    "nasa;small, sparse;5605;6538;1.17;35;0.006\n",
    "vchocyc;small, sparse;9491;10000;1.09;21;0.001\n",
    "xmark;small, sparse;6080;7051;1.16;38;0.014\n",
    "citeseer;large;693947;312383;0.45;13;0.000\n",
    "citeseerx;large;6540401;15011260;2.30;59;0.002\n",
    "Cit-patents;large;3774768;16518047;4.38;32;0.001\n",
    "Go-uniprot;large;6967956;34770235;4.99;21;0.000\n",
    "Uniprot22m;large;1595444;1595442;1.00;4;0.000\n",
    "Uniprot100m;large;16087295;16087293;1.00;9;0.000\n",
    "Uniprot150m;large;25037600;25037598;1.00;10;0.000\n",
    "email-EuAll;stanford;231000;223000;0.97;7;0.05\n",
    "p2p-Gnutella31;stanford;48000;55000;1.15;14;0.008\n",
    "soc-LiveJournal1;stanford;971000;1024000;1.05;24;0.21\n",
    "web-Google;stanford;372000;518000;1.39;34;0.15\n",
    "wiki-Talk;stanford;2282000;2312000;1.01;8;0.008\n",
    "\"\"\"\n",
    "csv_stream = io.StringIO(csv)\n",
    "\n",
    "table_rch=pd.read_csv(csv_stream,sep=';',index_col=0)\n",
    "print(table_rch['type'].unique())\n",
    "table_rch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee=np.geomspace(4000,25000000,num=10)\n",
    "ax=plt.gca()\n",
    "types =table_rch['type'].unique()\n",
    "colors=('blue', 'cyan', 'red', 'orange')\n",
    "for i, t in enumerate(types):\n",
    "  ax=table_rch[table_rch['type'] == t].plot.scatter(x='vertices',y='edges',ax=ax,\n",
    "                                                    logx=True,logy=True,grid=True,\n",
    "                                                    label=t,c=colors[i],\n",
    "                                                    title='Graphs from PReaCH paper, Table 1')\n",
    "ax.plot(ee,ee,'--',ee,2*ee,'-',label='dense vs sparse',c='gray')\n",
    "ann=('Uniprot150m','soc-LiveJournal1','wiki-Talk','Go','amaze','arXiv','Human','citeseer','citeseerx')\n",
    "for g in ann:\n",
    "  row = table_rch.loc[g]\n",
    "  ax.annotate(g, xy=(row['vertices'],row['edges']),\n",
    "              xytext=(5,-3), textcoords='offset points',horizontalalignment='left',\n",
    "              family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Datasets from O'Reach (`table_or`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Faster Reachability in Static Graphs\" (2020)  \n",
    "https://arxiv.org/abs/2008.10932"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To facilitate comparability, we adopt the instances used in the papers introducing PReaCH \\[22], GRAIL \\[35],\n",
    "> and TF \\[4], which are available either from the [GRAIL code repository][grail] or the Stanford Network Analysis\n",
    "> Platform SNAP \\[21]\n",
    "\n",
    "[grail]: https://code.google.com/archive/p/grail/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Table 2: Caption and headers](images/oreach/OReach_arXiv-table_2-datasets-caption_and_header.png)  \n",
    "![Table 2: Instances used in experiments](images/oreach/OReach_arXiv-table_2-datasets-real_graphs.png) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- $n/10^3$ - number of nodes (vertices) in 1000s\n",
    "- $m/10^3$ - number of edges in 1000s\n",
    "- $m/n$ - edge density, number of edges divided by number of nodes (vertices)\n",
    "- $\\mathcal{S}\\%$ - ratio of number of non-isolated sources to the number of nodes\n",
    "- $\\mathcal{T}\\%$ - ratio of number of non-isolated sinks to the number of nodes\n",
    "- $\\mathcal{I}\\%$ - ratio of number of isolated nodes to the total number of nodes\n",
    "- \\#WCC - number of weakly connected components\n",
    "- \\#WCC(large) - number of weakly connected components with more than $n/10$ nodes\n",
    "- $L_{\\text{max}}$ - maximum topological level, which is also length of the longest path, or maximal path length (equals diameter)\n",
    "- $\\rho\\%$ - the fraction of positive queries in a random sample of 100000 queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['large' 'small, dense' 'small, sparse' 'SNAP']\n"
     ]
    },
    {
     "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>type</th>\n",
       "      <th>vertices/1000</th>\n",
       "      <th>edges/1000</th>\n",
       "      <th>edges/vertices</th>\n",
       "      <th>sources [%]</th>\n",
       "      <th>sinks [%]</th>\n",
       "      <th>isolated [%]</th>\n",
       "      <th>WCCs</th>\n",
       "      <th>WCCs (large)</th>\n",
       "      <th>maxlevel</th>\n",
       "      <th>r-score [%]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graph</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>citeseer.scc</th>\n",
       "      <td>large</td>\n",
       "      <td>693.9</td>\n",
       "      <td>312.3</td>\n",
       "      <td>0.45</td>\n",
       "      <td>37.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>50.9</td>\n",
       "      <td>28663</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseerx</th>\n",
       "      <td>large</td>\n",
       "      <td>6540.4</td>\n",
       "      <td>15011.3</td>\n",
       "      <td>2.30</td>\n",
       "      <td>8.7</td>\n",
       "      <td>87.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47076</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0.1367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cit-Patents</th>\n",
       "      <td>large</td>\n",
       "      <td>3774.8</td>\n",
       "      <td>16518.9</td>\n",
       "      <td>4.38</td>\n",
       "      <td>13.7</td>\n",
       "      <td>44.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3627</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go_uniprot</th>\n",
       "      <td>large</td>\n",
       "      <td>6968.0</td>\n",
       "      <td>34769.3</td>\n",
       "      <td>4.99</td>\n",
       "      <td>99.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprotenc_22m</th>\n",
       "      <td>large</td>\n",
       "      <td>1595.4</td>\n",
       "      <td>1595.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprotenc_100m</th>\n",
       "      <td>large</td>\n",
       "      <td>16087.3</td>\n",
       "      <td>16087.3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>90.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniprotenc_150m</th>\n",
       "      <td>large</td>\n",
       "      <td>25037.6</td>\n",
       "      <td>25037.6</td>\n",
       "      <td>1.00</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>go_sub</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6.8</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1.97</td>\n",
       "      <td>0.9</td>\n",
       "      <td>45.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.2258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pubmed_sub</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.45</td>\n",
       "      <td>29.0</td>\n",
       "      <td>52.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>0.6458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yago_sub</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6.6</td>\n",
       "      <td>42.4</td>\n",
       "      <td>6.38</td>\n",
       "      <td>77.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.1506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseer_sub</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>10.7</td>\n",
       "      <td>44.3</td>\n",
       "      <td>4.13</td>\n",
       "      <td>42.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>0.3672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arXiv</th>\n",
       "      <td>small, dense</td>\n",
       "      <td>6.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>11.12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>15.4643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amaze</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.6</td>\n",
       "      <td>0.97</td>\n",
       "      <td>32.1</td>\n",
       "      <td>41.8</td>\n",
       "      <td>9.9</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>17.2337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kegg</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.22</td>\n",
       "      <td>32.6</td>\n",
       "      <td>45.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>20.1636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nasa</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>5.6</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>0.5284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xmark</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>6.1</td>\n",
       "      <td>7.1</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1.4513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vchocyc</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.3</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.1517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtbrv</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>9.6</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.1511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>anthra</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.1</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ecoo</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>12.6</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>0.1088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agrocyc</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>12.7</td>\n",
       "      <td>13.4</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human</th>\n",
       "      <td>small, sparse</td>\n",
       "      <td>38.8</td>\n",
       "      <td>39.6</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p2p-Gnutella31</th>\n",
       "      <td>SNAP</td>\n",
       "      <td>48.4</td>\n",
       "      <td>55.3</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.6</td>\n",
       "      <td>95.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email-EuAll</th>\n",
       "      <td>SNAP</td>\n",
       "      <td>230.8</td>\n",
       "      <td>223.0</td>\n",
       "      <td>0.97</td>\n",
       "      <td>82.6</td>\n",
       "      <td>17.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15631</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>web-Google</th>\n",
       "      <td>SNAP</td>\n",
       "      <td>371.8</td>\n",
       "      <td>517.8</td>\n",
       "      <td>1.39</td>\n",
       "      <td>43.7</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2585</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>14.8090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soc-LiveJournal1</th>\n",
       "      <td>SNAP</td>\n",
       "      <td>970.3</td>\n",
       "      <td>1024.1</td>\n",
       "      <td>1.06</td>\n",
       "      <td>39.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>521</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>5.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wiki-Talk</th>\n",
       "      <td>SNAP</td>\n",
       "      <td>2281.9</td>\n",
       "      <td>2311.6</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.1</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2487</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0.8117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           type  vertices/1000  edges/1000  edges/vertices  \\\n",
       "Graph                                                                        \n",
       "citeseer.scc              large          693.9       312.3            0.45   \n",
       "citeseerx                 large         6540.4     15011.3            2.30   \n",
       "cit-Patents               large         3774.8     16518.9            4.38   \n",
       "go_uniprot                large         6968.0     34769.3            4.99   \n",
       "uniprotenc_22m            large         1595.4      1595.4            1.00   \n",
       "uniprotenc_100m           large        16087.3     16087.3            1.00   \n",
       "uniprotenc_150m           large        25037.6     25037.6            1.00   \n",
       "go_sub             small, dense            6.8        13.4            1.97   \n",
       "pubmed_sub         small, dense            9.0        40.0            4.45   \n",
       "yago_sub           small, dense            6.6        42.4            6.38   \n",
       "citeseer_sub       small, dense           10.7        44.3            4.13   \n",
       "arXiv              small, dense            6.0        66.7           11.12   \n",
       "amaze             small, sparse            3.7         3.6            0.97   \n",
       "kegg              small, sparse            3.6         4.4            1.22   \n",
       "nasa              small, sparse            5.6         6.5            1.17   \n",
       "xmark             small, sparse            6.1         7.1            1.16   \n",
       "vchocyc           small, sparse            9.5        10.3            1.09   \n",
       "mtbrv             small, sparse            9.6        10.4            1.09   \n",
       "anthra            small, sparse           12.5        13.1            1.05   \n",
       "ecoo              small, sparse           12.6        13.4            1.06   \n",
       "agrocyc           small, sparse           12.7        13.4            1.06   \n",
       "human             small, sparse           38.8        39.6            1.02   \n",
       "p2p-Gnutella31             SNAP           48.4        55.3            1.14   \n",
       "email-EuAll                SNAP          230.8       223.0            0.97   \n",
       "web-Google                 SNAP          371.8       517.8            1.39   \n",
       "soc-LiveJournal1           SNAP          970.3      1024.1            1.06   \n",
       "wiki-Talk                  SNAP         2281.9      2311.6            1.01   \n",
       "\n",
       "                  sources [%]  sinks [%]  isolated [%]   WCCs  WCCs (large)  \\\n",
       "Graph                                                                         \n",
       "citeseer.scc             37.5        4.1          50.9  28663             1   \n",
       "citeseerx                 8.7       87.8           0.0  47076             1   \n",
       "cit-Patents              13.7       44.6           0.0   3627             1   \n",
       "go_uniprot               99.7        0.0           0.0      1             1   \n",
       "uniprotenc_22m           97.5        0.0           0.0      1             1   \n",
       "uniprotenc_100m          90.7        0.0           0.0      1             1   \n",
       "uniprotenc_150m          86.5        0.0           0.0      1             1   \n",
       "go_sub                    0.9       45.4           0.0      1             1   \n",
       "pubmed_sub               29.0       52.2           0.0      1             1   \n",
       "yago_sub                 77.9        4.0           0.0      1             1   \n",
       "citeseer_sub             42.6       17.4           0.0      1             1   \n",
       "arXiv                    16.0       10.4           0.0      1             1   \n",
       "amaze                    32.1       41.8           9.9     22             1   \n",
       "kegg                     32.6       45.2           0.1     22             1   \n",
       "nasa                      0.0       55.6           0.0      1             1   \n",
       "xmark                     0.0       58.3           0.0      1             1   \n",
       "vchocyc                   0.0       92.8           0.0      1             1   \n",
       "mtbrv                     0.0       93.0           0.0      1             1   \n",
       "anthra                    0.0       94.7           0.0      2             1   \n",
       "ecoo                      0.0       94.1           0.0      1             1   \n",
       "agrocyc                   0.0       94.1           0.0      1             1   \n",
       "human                     0.0       98.1           0.0      1             1   \n",
       "p2p-Gnutella31            0.6       95.4           0.0     12             1   \n",
       "email-EuAll              82.6       17.3           0.0  15631             1   \n",
       "web-Google               43.7       37.9           0.0   2585             1   \n",
       "soc-LiveJournal1         39.9       57.7           0.0    521             1   \n",
       "wiki-Talk                 1.1       98.5           0.0   2487             1   \n",
       "\n",
       "                  maxlevel  r-score [%]  \n",
       "Graph                                    \n",
       "citeseer.scc            13       0.0002  \n",
       "citeseerx               59       0.1367  \n",
       "cit-Patents             32       0.0409  \n",
       "go_uniprot              20       0.0004  \n",
       "uniprotenc_22m           4       0.0001  \n",
       "uniprotenc_100m          9       0.0000  \n",
       "uniprotenc_150m         10       0.0000  \n",
       "go_sub                  16       0.2258  \n",
       "pubmed_sub              19       0.6458  \n",
       "yago_sub                13       0.1506  \n",
       "citeseer_sub            36       0.3672  \n",
       "arXiv                  167      15.4643  \n",
       "amaze                   16      17.2337  \n",
       "kegg                    26      20.1636  \n",
       "nasa                    35       0.5284  \n",
       "xmark                   38       1.4513  \n",
       "vchocyc                 21       0.1517  \n",
       "mtbrv                   22       0.1511  \n",
       "anthra                  16       0.0951  \n",
       "ecoo                    22       0.1088  \n",
       "agrocyc                 16       0.1060  \n",
       "human                   18       0.0231  \n",
       "p2p-Gnutella31          14       0.7725  \n",
       "email-EuAll              7       5.0732  \n",
       "web-Google              34      14.8090  \n",
       "soc-LiveJournal1        24       5.3781  \n",
       "wiki-Talk                8       0.8117  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title Table 2: Instances used for experimets from O'Reach (table_or) { form-width: \"25%\" }\n",
    "\n",
    "csv=u\"\"\"Graph;type;source;vertices/1000;edges/1000;edges/vertices;sources [%];sinks [%];isolated [%];WCCs;WCCs (large);maxlevel;r-score [%]\n",
    "citeseer.scc;large;citation network;693.9;312.3;0.45;37.5;4.1;50.9;28663;1;13;0.0002\n",
    "citeseerx;large;citation network;6540.4;15011.3;2.30;8.7;87.8;0.0;47076;1;59;0.1367\n",
    "cit-Patents;large;citation network;3774.8;16518.9;4.38;13.7;44.6;0.0;3627;1;32;0.0409\n",
    "go_uniprot;large;taxonomy graph;6968.0;34769.3;4.99;99.7;0.0;0.0;1;1;20;0.0004\n",
    "uniprotenc_22m;large;RDF graph of protein database;1595.4;1595.4;1.00;97.5;0.0;0.0;1;1;4;0.0001\n",
    "uniprotenc_100m;large;RDF graph of protein database;16087.3;16087.3;1.00;90.7;0.0;0.0;1;1;9;0.0000\n",
    "uniprotenc_150m;large;RDF graph of protein database;25037.6;25037.6;1.00;86.5;0.0;0.0;1;1;10;0.0000\n",
    "\n",
    "go_sub;small, dense;taxonomy graph;6.8;13.4;1.97;0.9;45.4;0.0;1;1;16;0.2258\n",
    "pubmed_sub;small, dense;citation network;9.0;40.0;4.45;29.0;52.2;0.0;1;1;19;0.6458\n",
    "yago_sub;small, dense;semantic knowledge database;6.6;42.4;6.38;77.9;4.0;0.0;1;1;13;0.1506\n",
    "citeseer_sub;small, dense;citation network;10.7;44.3;4.13;42.6;17.4;0.0;1;1;36;0.3672\n",
    "arXiv;small, dense;citation network;6.0;66.7;11.12;16.0;10.4;0.0;1;1;167;15.4643\n",
    "\n",
    "amaze;small, sparse;metabolic network;3.7;3.6;0.97;32.1;41.8;9.9;22;1;16;17.2337\n",
    "kegg;small, sparse;metabolic network;3.6;4.4;1.22;32.6;45.2;0.1;22;1;26;20.1636\n",
    "nasa;small, sparse;XML documents;5.6;6.5;1.17;0.0;55.6;0.0;1;1;35;0.5284\n",
    "xmark;small, sparse;XML documents;6.1;7.1;1.16;0.0;58.3;0.0;1;1;38;1.4513\n",
    "vchocyc;small, sparse;pathway and genome database;9.5;10.3;1.09;0.0;92.8;0.0;1;1;21;0.1517\n",
    "mtbrv;small, sparse;pathway and genome database;9.6;10.4;1.09;0.0;93.0;0.0;1;1;22;0.1511\n",
    "anthra;small, sparse;pathway and genome database;12.5;13.1;1.05;0.0;94.7;0.0;2;1;16;0.0951\n",
    "ecoo;small, sparse;pathway and genome database;12.6;13.4;1.06;0.0;94.1;0.0;1;1;22;0.1088\n",
    "agrocyc;small, sparse;pathway and genome database;12.7;13.4;1.06;0.0;94.1;0.0;1;1;16;0.1060\n",
    "human;small, sparse;pathway and genome database;38.8;39.6;1.02;0.0;98.1;0.0;1;1;18;0.0231\n",
    "\n",
    "p2p-Gnutella31;SNAP;peer-to-peer network;48.4;55.3;1.14;0.6;95.4;0.0;12;1;14;0.7725\n",
    "email-EuAll;SNAP;e-mail network graph;230.8;223.0;0.97;82.6;17.3;0.0;15631;1;7;5.0732\n",
    "web-Google;SNAP;web graph;371.8;517.8;1.39;43.7;37.9;0.0;2585;1;34;14.8090\n",
    "soc-LiveJournal1;SNAP;social network;970.3;1024.1;1.06;39.9;57.7;0.0;521;1;24;5.3781\n",
    "wiki-Talk;SNAP;communication network;2281.9;2311.6;1.01;1.1;98.5;0.0;2487;1;8;0.8117\n",
    "\"\"\"\n",
    "csv_stream = io.StringIO(csv)\n",
    "\n",
    "table_or=pd.read_csv(csv_stream,sep=';',index_col=0)\n",
    "print(table_or['type'].unique())\n",
    "table_or.drop('source', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>vertices/1000</th>\n",
       "      <th>edges/1000</th>\n",
       "      <th>edges/vertices</th>\n",
       "      <th>sources [%]</th>\n",
       "      <th>sinks [%]</th>\n",
       "      <th>isolated [%]</th>\n",
       "      <th>WCCs</th>\n",
       "      <th>WCCs (large)</th>\n",
       "      <th>maxlevel</th>\n",
       "      <th>r-score [%]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2398.311111</td>\n",
       "      <td>4214.537037</td>\n",
       "      <td>2.208519</td>\n",
       "      <td>30.862963</td>\n",
       "      <td>49.644444</td>\n",
       "      <td>2.255556</td>\n",
       "      <td>3728.296296</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.481481</td>\n",
       "      <td>3.107011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5701.997573</td>\n",
       "      <td>9035.370188</td>\n",
       "      <td>2.355097</td>\n",
       "      <td>35.359028</td>\n",
       "      <td>37.259695</td>\n",
       "      <td>9.906227</td>\n",
       "      <td>10621.160634</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.505195</td>\n",
       "      <td>6.077715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.600000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>13.250000</td>\n",
       "      <td>1.015000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.850000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.500000</td>\n",
       "      <td>0.068000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.700000</td>\n",
       "      <td>44.300000</td>\n",
       "      <td>1.090000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>45.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.151700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1282.850000</td>\n",
       "      <td>1309.750000</td>\n",
       "      <td>2.135000</td>\n",
       "      <td>43.150000</td>\n",
       "      <td>92.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>271.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.131500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25037.600000</td>\n",
       "      <td>34769.300000</td>\n",
       "      <td>11.120000</td>\n",
       "      <td>99.700000</td>\n",
       "      <td>98.500000</td>\n",
       "      <td>50.900000</td>\n",
       "      <td>47076.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>20.163600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       vertices/1000    edges/1000  edges/vertices  sources [%]  sinks [%]  \\\n",
       "count      27.000000     27.000000       27.000000    27.000000  27.000000   \n",
       "mean     2398.311111   4214.537037        2.208519    30.862963  49.644444   \n",
       "std      5701.997573   9035.370188        2.355097    35.359028  37.259695   \n",
       "min         3.600000      3.600000        0.450000     0.000000   0.000000   \n",
       "25%         7.900000     13.250000        1.015000     0.000000  13.850000   \n",
       "50%        12.700000     44.300000        1.090000    16.000000  45.400000   \n",
       "75%      1282.850000   1309.750000        2.135000    43.150000  92.900000   \n",
       "max     25037.600000  34769.300000       11.120000    99.700000  98.500000   \n",
       "\n",
       "       isolated [%]          WCCs  WCCs (large)    maxlevel  r-score [%]  \n",
       "count     27.000000     27.000000          27.0   27.000000    27.000000  \n",
       "mean       2.255556   3728.296296           1.0   26.481481     3.107011  \n",
       "std        9.906227  10621.160634           0.0   30.505195     6.077715  \n",
       "min        0.000000      1.000000           1.0    4.000000     0.000000  \n",
       "25%        0.000000      1.000000           1.0   13.500000     0.068000  \n",
       "50%        0.000000      1.000000           1.0   19.000000     0.151700  \n",
       "75%        0.000000    271.500000           1.0   29.000000     1.131500  \n",
       "max       50.900000  47076.000000           1.0  167.000000    20.163600  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_or.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee = np.geomspace(3,25000,num=10)\n",
    "fig = plt.figure(figsize=(13,8))\n",
    "ax = fig.gca()\n",
    "types  = table_or['type'].unique()\n",
    "colors = ('blue', 'cyan', 'red', 'orange')\n",
    "color_map = {\n",
    "    'small, dense': 'blue',\n",
    "    'small, sparse': 'cyan',\n",
    "    'large': 'red',\n",
    "    'SNAP': 'orange',\n",
    "}\n",
    "for t in types:\n",
    "  ax=table_or[table_or['type'] == t].plot.scatter(x='vertices/1000',y='edges/1000',ax=ax,\n",
    "                                                  logx=True,logy=True,grid=True,\n",
    "                                                  label=t,c=color_map[t],\n",
    "                                                  title='Graphs from O\\'Reach paper, Table 2')\n",
    "ax.plot(ee,ee,'--',ee,2*ee,'-',label='dense vs sparse',c='gray')\n",
    "ax.plot(ee,1000*ee*ee,':',label='max edges',c='0.5')\n",
    "#ann=('Uniprot150m','soc-LiveJournal1','wiki-Talk','Go','amaze','arXiv','Human','citeseer','citeseerx')\n",
    "#for g in ann:\n",
    "#  row = table_rch.loc[g]\n",
    "#  ax.annotate(g, xy=(row['vertices'],row['edges']),\n",
    "#              xytext=(5,-3), textcoords='offset points',horizontalalignment='left',\n",
    "#              family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "for row in table_or.itertuples():\n",
    "    ax.annotate(row[0], xy=(row[3],row[4]),\n",
    "                xytext=(-3,+3), textcoords='offset points',horizontalalignment='right',\n",
    "                family='sans-serif', fontsize=9, color='darkslategrey')\n",
    "#ax.set_xlim(left  =2e3)\n",
    "#ax.set_ylim(bottom=5e3)\n",
    "ax.set_ylim(top=1e5)\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ee = np.geomspace(4,25000,num=10)\n",
    "#fig = plt.figure(figsize=(13,8))\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "types  = table_or['type'].unique()\n",
    "colors = ('blue', 'cyan', 'red', 'orange')\n",
    "color_map = {\n",
    "    'small, dense': 'blue',\n",
    "    'small, sparse': 'cyan',\n",
    "    'large': 'red',\n",
    "    'SNAP': 'orange',\n",
    "}\n",
    "for t in types:\n",
    "  ax=table_or[table_or['type'] == t].plot.scatter(x='sources [%]',y='sinks [%]',ax=ax,\n",
    "                                                  logx=False,logy=False,grid=True,\n",
    "                                                  label=t,c=color_map[t],\n",
    "                                                  title='Graphs from O\\'Reach paper, Table 2')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}