{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random graphs\n",
    "\n",
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/yy/netsci-course/blob/master/docs/m08-randomgraphs/lab08.ipynb\">\n",
    "        <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" style=\"vertical-align: -8px; margin-right: 10px;\" />\n",
    "        Open this notebook in Google Colab\n",
    "    </a>\n",
    "</div>\n",
    "\n",
    "\n",
    "<div style=\"display: flex; align-items: center;\">\n",
    "    <a href=\"https://raw.githubusercontent.com/yy/netsci-course/master/docs/m08-randomgraphs/lab08.ipynb\" download>\n",
    "        <img src=\"http://yyahn.com/netsci-course/images/download_icon.png\" style=\"vertical-align: -8px; margin-right: 10px;\" />\n",
    "        Download this notebook (File -> Save As)\n",
    "    </a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the random graph models?\n",
    "\n",
    "\"Random graph models\" may refer a wide range of models where randomness is involved in the generation of the graph, particularly in the edge formation. But, the most basic random graph models — that are usually referred as random graph models — are the Erdős-Rényi (ER) model and the configuration model. They both are commonly used as \"null models\" — the models that show us what we would expect if we don't know anything about the graph other than basic properties of the graphs — in network science. \n",
    "\n",
    "## Why random graphs?\n",
    "\n",
    "Probably the most important reason to study random graphs is that they provide a baseline for comparison, or act as \"null models\". Let's say you collected a network data and found that the average clustering coefficient of the network is 0.5. Is this high or low? \n",
    "\n",
    "<img src=\"https://yyiki.s3.us-east-2.amazonaws.com/public/imgs/is_four_a_lot-depends_on_contexts.jpg\" width=\"400\" />\n",
    "\n",
    "If the network is large and very sparse, 0.5 may be almost impossibly high. On the other hand, if the network is extremely dense, then 0.5 may be extremely low. So, it is hard to say! What could be useful contexts? \n",
    "\n",
    "## ER random graph\n",
    "\n",
    "One useful context can be an ER random graph with the same number of nodes and edges. This model answers the following question: \"if we only know the number of nodes and edges, and nothing else, what would be the average clustering coefficient that we would expect?\" We can create many instances of ER random graphs and calculate the average clustering coefficient. Then, we can compare the average clustering coefficient of the real network with the distribution of the average clustering coefficient of the ER random graphs!\n",
    "\n",
    "Let's try with the dolphins network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "dolphin_net = nx.read_gml(\"dolphins.gml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: Shall we measure the average clustering coefficent of this network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average clustering coefficient: 0.259\n"
     ]
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's then create many instances of ER random graphs with the exactly same number of nodes and edges as the dolphins network, and calculate the average clustering coefficient.\n",
    "\n",
    "**Q: plot the distribution of the average clustering coefficient of many (say 1,000) ER random graphs.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AKE1OBaOhQ4dqwYIF6tatm5o2bSoPDw9X1wUAAFDqnApGS5cu1bvvvquuXbu6uh4AAAC3cepyfR8fH9WrV8/VtQAAALiVU8Fo5MiRev3113UDF7QBAACUOU59lfbFF19ow4YN+uijj9SkSRN5e3s7bF++fLlLigMAAChNTgWjoKAgPfDAA66uBQAAwK2cCkbz5893dR0AAABu5/QPPObm5mrjxo06cuSI+vbtK39/f50+fVoBAQGqWrWqK2uskFzxa9cAAMC1nApGx48fV5cuXXTixAllZ2frj3/8o/z9/TVt2jRlZ2crMTHR1XUCAACUOKeuShs6dKjatGmjc+fOyc/Pz97+wAMPaN26dS4rDgAAoDQ5FYw+//xzvfDCC/Lx8XFoj4qK0nfffeeSwqSfbz0yYcIE1alTR35+fqpbt65efPFFh58JMMZo4sSJCg8Pl5+fn2JiYnTo0CGX1QAAAG4eTgWj/Px85eXlFWg/deqU/P39b7ioq6ZNm6a5c+fqjTfe0L59+zRt2jRNnz5ds2fPtveZPn26Zs2apcTERG3dulVVqlRRbGysLl++7LI6AADAzcGpYHTvvfdq5syZ9nUPDw9lZWVp0qRJLr1NyJdffqkePXqoW7duioqK0kMPPaR7771X27Ztk/Tz2aKZM2fqhRdeUI8ePdS8eXMtWrRIp0+f1sqVK4vcb3Z2tjIzMx0WAAAAp4LRq6++qs2bN+u2227T5cuX1bdvX/vXaNOmTXNZce3atdO6det08OBBSdLu3bv1xRdf6L777pMkpaSkKC0tTTExMfbHBAYGqm3bttqyZUuR+01ISFBgYKB9iYyMdFnNAACg/HLqqrSaNWtq9+7dWrp0qfbs2aOsrCzFx8crLi7OYTL2jRo7dqwyMzPVqFEjVapUSXl5eXrppZcUFxcnSUpLS5MkhYaGOjwuNDTUvq0w48aN04gRI+zrmZmZhCMAAOD87xh5eXnpsccec2UtBbz77rtavHixlixZoiZNmig5OVnDhg1TRESE+vfv7/R+bTabbDabCysFAAAVgVPBaNGiRdfc/vjjjztVzK89//zzGjt2rPr06SNJatasmY4fP66EhAT1799fYWFhkqT09HSFh4fbH5eenq4WLVq4pAYAAHDzcCoYDR061GE9JydHly5dko+PjypXruyyYHTp0iV5ejpOg6pUqZLy8/MlSXXq1FFYWJjWrVtnD0KZmZnaunWrnnnmGZfUAAAAbh5OBaNz584VaDt06JCeeeYZPf/88zdc1FXdu3fXSy+9pFq1aqlJkybatWuXZsyYoT/96U+Sfr4abtiwYZo8ebLq16+vOnXqaMKECYqIiFDPnj1dVgcAALg5OD3H6Nfq16+vqVOn6rHHHtP+/ftdss/Zs2drwoQJGjRokM6cOaOIiAg99dRTmjhxor3P6NGjdfHiRQ0cOFDnz59Xhw4dtGbNGvn6+rqkBgAAcPPwML/8GekblJycrD/84Q/l7neBMjMzFRgYqIyMDAUEBJTKMV19E9ljU7u5dH8AAJR1JfH57dQZo/fff99h3Rij1NRUvfHGG2rfvr1LCgMAAChtTgWjX8/f8fDw0C233KJ77rlHr776qivqAgAAKHVOBaOrV4Wh7Pj1V3N8tQYAQPE5dUsQAACAisipM0a/vJ3Gb5kxY4YzhwAAACh1TgWjXbt2adeuXcrJyVHDhg0lSQcPHlSlSpXUqlUrez8PDw/XVAkAAFAKnApG3bt3l7+/vxYuXKhq1apJ+vlHH5988kndddddGjlypEuLBAAAKA1OzTF69dVXlZCQYA9FklStWjVNnjyZq9IAAEC55VQwyszM1Pfff1+g/fvvv9eFCxduuCgAAAB3cCoYPfDAA3ryySe1fPlynTp1SqdOndJ///tfxcfH68EHH3R1jQAAAKXCqTlGiYmJGjVqlPr27aucnJyfd+Tlpfj4eL388ssuLRAAAKC0OBWMKleurDfffFMvv/yyjhw5IkmqW7euqlSp4tLiAAAAStMN/cBjamqqUlNTVb9+fVWpUkUuvB8tAABAqXMqGP3444/q3LmzGjRooK5duyo1NVWSFB8fz6X6AACg3HIqGA0fPlze3t46ceKEKleubG/v3bu31qxZ47LiAAAASpNTc4w++eQTffzxx6pZs6ZDe/369XX8+HGXFAYAAFDanDpjdPHiRYczRVedPXtWNpvthosCAABwB6eC0V133aVFixbZ1z08PJSfn6/p06erU6dOLisOAACgNDn1Vdr06dPVuXNn7dixQ1euXNHo0aP17bff6uzZs9q8ebOrawQAACgVTp0xatq0qQ4ePKgOHTqoR48eunjxoh588EHt2rVLdevWdXWNAAAApaLYZ4xycnLUpUsXJSYmavz48SVREwAAgFsU+4yRt7e39uzZUxK1AAAAuJVTX6U99thjmjdvnqtrAQAAcCunJl/n5ubqnXfe0aeffqrWrVsXuEfajBkzXFIcAABAaSpWMDp69KiioqL0zTffqFWrVpKkgwcPOvTx8PBwXXUAAAClqFjBqH79+kpNTdWGDRsk/XwLkFmzZik0NLREigMAAChNxZpjZIxxWP/oo4908eJFlxYEAADgLk5Nvr7q10EJAACgPCtWMPLw8Cgwh4g5RQAAoKIo1hwjY4yeeOIJ+41iL1++rKeffrrAVWnLly93XYUAAAClpFjBqH///g7rjz32mEuLAQAAcKdiBaP58+eXVB0AAABud0OTrwEAACoSghEAAICFYAQAAGAp88Hou+++02OPPabq1avLz89PzZo1044dO+zbjTGaOHGiwsPD5efnp5iYGB06dMiNFQMAgPKqTAejc+fOqX379vL29tZHH32kvXv36tVXX1W1atXsfaZPn65Zs2YpMTFRW7duVZUqVRQbG6vLly+7sXIAAFAeFeuqtNI2bdo0RUZGOlwNV6dOHfvfxhjNnDlTL7zwgnr06CFJWrRokUJDQ7Vy5Ur16dOn1GsGAADlV5k+Y/T++++rTZs2evjhhxUSEqKWLVvq7bfftm9PSUlRWlqaYmJi7G2BgYFq27attmzZUuR+s7OzlZmZ6bAAAACU6WB09OhRzZ07V/Xr19fHH3+sZ555Rs8995wWLlwoSUpLS5MkhYaGOjwuNDTUvq0wCQkJCgwMtC+RkZEl9yQAAEC5UaaDUX5+vlq1aqUpU6aoZcuWGjhwoAYMGKDExMQb2u+4ceOUkZFhX06ePOmiigEAQHlWpoNReHi4brvtNoe2xo0b68SJE5KksLAwSVJ6erpDn/T0dPu2wthsNgUEBDgsAAAAZToYtW/fXgcOHHBoO3jwoGrXri3p54nYYWFhWrdunX17Zmamtm7dqujo6FKtFQAAlH9l+qq04cOHq127dpoyZYoeeeQRbdu2TW+99ZbeeustSZKHh4eGDRumyZMnq379+qpTp44mTJigiIgI9ezZ073FAwCAcqdMB6M77rhDK1as0Lhx4/T3v/9dderU0cyZMxUXF2fvM3r0aF28eFEDBw7U+fPn1aFDB61Zs0a+vr5urBwAAJRHHsYY4+4i3C0zM1OBgYHKyMgotflGUWNXl+j+j03tVqL7BwDA3Uri87tMzzECAAAoTQQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACxl+pYgFUlJ/9I1AAC4cZwxAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMDi5e4CUDKixq62/31sajc3VgIAQPnBGSMAAAALwQgAAMBCMAIAALAQjAAAACzlKhhNnTpVHh4eGjZsmL3t8uXLGjx4sKpXr66qVauqV69eSk9Pd1+RAACg3Co3wWj79u36xz/+oebNmzu0Dx8+XB988IGWLVumzz77TKdPn9aDDz7opioBAEB5Vi6CUVZWluLi4vT222+rWrVq9vaMjAzNmzdPM2bM0D333KPWrVtr/vz5+vLLL/XVV1+5sWIAAFAelYtgNHjwYHXr1k0xMTEO7UlJScrJyXFob9SokWrVqqUtW7YUub/s7GxlZmY6LAAAAGX+Bx6XLl2qnTt3avv27QW2paWlycfHR0FBQQ7toaGhSktLK3KfCQkJ+tvf/ubqUgEAQDlXps8YnTx5UkOHDtXixYvl6+vrsv2OGzdOGRkZ9uXkyZMu2zcAACi/ynQwSkpK0pkzZ9SqVSt5eXnJy8tLn332mWbNmiUvLy+FhobqypUrOn/+vMPj0tPTFRYWVuR+bTabAgICHBYAAIAy/VVa586d9fXXXzu0Pfnkk2rUqJHGjBmjyMhIeXt7a926derVq5ck6cCBAzpx4oSio6PdUTIAACjHynQw8vf3V9OmTR3aqlSpourVq9vb4+PjNWLECAUHBysgIEDPPvusoqOj9fvf/94dJQMAgHKsTAej6/Haa6/J09NTvXr1UnZ2tmJjY/Xmm2+6uywAAFAOeRhjjLuLcLfMzEwFBgYqIyOjxOYbRY1dXSL7vR7HpnZz27EBACgpJfH5XaYnXwMAAJQmghEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYCEYAQAAWAhGAAAAFoIRAACAhWAEAABgIRgBAABYCEYAAAAWghEAAICFYAQAAGAhGAEAAFi83F0ASl7U2NX2v49N7ebGSgAAKNs4YwQAAGAhGAEAAFgIRgAAABaCEQAAgIXJ1yXol5OeAQBA2ccZIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwFKmg1FCQoLuuOMO+fv7KyQkRD179tSBAwcc+ly+fFmDBw9W9erVVbVqVfXq1Uvp6eluqhgAAJRnZToYffbZZxo8eLC++uorrV27Vjk5Obr33nt18eJFe5/hw4frgw8+0LJly/TZZ5/p9OnTevDBB91YddkWNXa1wwIAAP6Pl7sLuJY1a9Y4rC9YsEAhISFKSkrSH/7wB2VkZGjevHlasmSJ7rnnHknS/Pnz1bhxY3311Vf6/e9/X+h+s7OzlZ2dbV/PzMwsuScBAADKjTJ9xujXMjIyJEnBwcGSpKSkJOXk5CgmJsbep1GjRqpVq5a2bNlS5H4SEhIUGBhoXyIjI0u2cAAAUC6Um2CUn5+vYcOGqX379mratKkkKS0tTT4+PgoKCnLoGxoaqrS0tCL3NW7cOGVkZNiXkydPlmTpAACgnCjTX6X90uDBg/XNN9/oiy++uOF92Ww22Ww2F1QFAAAqknJxxmjIkCFatWqVNmzYoJo1a9rbw8LCdOXKFZ0/f96hf3p6usLCwkq5SgAAUN6V6WBkjNGQIUO0YsUKrV+/XnXq1HHY3rp1a3l7e2vdunX2tgMHDujEiROKjo4u7XIBAEA5V6a/Shs8eLCWLFmi9957T/7+/vZ5Q4GBgfLz81NgYKDi4+M1YsQIBQcHKyAgQM8++6yio6OLvCINAACgKGU6GM2dO1eS1LFjR4f2+fPn64knnpAkvfbaa/L09FSvXr2UnZ2t2NhYvfnmm6VcKQAAqAjKdDAyxvxmH19fX82ZM0dz5swphYoAAEBFVqbnGAEAAJQmghEAAICFYAQAAGAhGAEAAFjK9OTr8og71gMAUH5xxggAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwEIwAgAAsHC5/k3ulz8vcGxqNzdWAgCA+3HGCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMBCMAIAALAQjAAAACwEIwAAAAvBCAAAwOLl7gLKu1/enR4AAJRvnDECAACwcMYIdtc6+3VsardSrAQAAPfgjBEAAICFYAQAAGAhGAEAAFgIRgAAABaCEQAAgIVgBAAAYOFyfVyXX17Kz6X7AICKqsKcMZozZ46ioqLk6+urtm3batu2be4uCQAAlDMV4ozRf/7zH40YMUKJiYlq27atZs6cqdjYWB04cEAhISHuLq/C4YcgAQAVVYU4YzRjxgwNGDBATz75pG677TYlJiaqcuXKeuedd9xdGgAAKEfK/RmjK1euKCkpSePGjbO3eXp6KiYmRlu2bCn0MdnZ2crOzravZ2RkSJIyMzOLffz87EvFfkxF5sxrCACAM65+5hhjXLbPch+MfvjhB+Xl5Sk0NNShPTQ0VPv37y/0MQkJCfrb3/5WoD0yMrJEaryZBM50dwUAgJvNjz/+qMDAQJfsq9wHI2eMGzdOI0aMsK+fP39etWvX1okTJ1z2wsI5mZmZioyM1MmTJxUQEODucm5qjEXZwViUHYxF2ZKRkaFatWopODjYZfss98GoRo0aqlSpktLT0x3a09PTFRYWVuhjbDabbDZbgfbAwEDe6GVEQEAAY1FGMBZlB2NRdjAWZYunp+umTJf7ydc+Pj5q3bq11q1bZ2/Lz8/XunXrFB0d7cbKAABAeVPuzxhJ0ogRI9S/f3+1adNGd955p2bOnKmLFy/qySefdHdpAACgHKkQwah37976/vvvNXHiRKWlpalFixZas2ZNgQnZRbHZbJo0aVKhX6+hdDEWZQdjUXYwFmUHY1G2lMR4eBhXXuMGAABQjpX7OUYAAACuQjACAACwEIwAAAAsBCMAAADLTROM5syZo6ioKPn6+qpt27batm3bNfsvW7ZMjRo1kq+vr5o1a6YPP/ywlCqt+IozFm+//bbuuusuVatWTdWqVVNMTMxvjh2uX3H/XVy1dOlSeXh4qGfPniVb4E2kuGNx/vx5DR48WOHh4bLZbGrQoAH/nXKR4o7FzJkz1bBhQ/n5+SkyMlLDhw/X5cuXS6naimvTpk3q3r27IiIi5OHhoZUrV/7mYzZu3KhWrVrJZrOpXr16WrBgQfEPbG4CS5cuNT4+Puadd94x3377rRkwYIAJCgoy6enphfbfvHmzqVSpkpk+fbrZu3eveeGFF4y3t7f5+uuvS7nyiqe4Y9G3b18zZ84cs2vXLrNv3z7zxBNPmMDAQHPq1KlSrrziKe5YXJWSkmJ+97vfmbvuusv06NGjdIqt4Io7FtnZ2aZNmzama9eu5osvvjApKSlm48aNJjk5uZQrr3iKOxaLFy82NpvNLF682KSkpJiPP/7YhIeHm+HDh5dy5RXPhx9+aMaPH2+WL19uJJkVK1Zcs//Ro0dN5cqVzYgRI8zevXvN7NmzTaVKlcyaNWuKddybIhjdeeedZvDgwfb1vLw8ExERYRISEgrt/8gjj5hu3bo5tLVt29Y89dRTJVrnzaC4Y/Frubm5xt/f3yxcuLCkSrxpODMWubm5pl27duaf//yn6d+/P8HIRYo7FnPnzjW33nqruXLlSmmVeNMo7lgMHjzY3HPPPQ5tI0aMMO3bty/ROm821xOMRo8ebZo0aeLQ1rt3bxMbG1usY1X4r9KuXLmipKQkxcTE2Ns8PT0VExOjLVu2FPqYLVu2OPSXpNjY2CL74/o4Mxa/dunSJeXk5Lj0hoE3I2fH4u9//7tCQkIUHx9fGmXeFJwZi/fff1/R0dEaPHiwQkND1bRpU02ZMkV5eXmlVXaF5MxYtGvXTklJSfav244ePaoPP/xQXbt2LZWa8X9c9dldIX75+lp++OEH5eXlFfgV7NDQUO3fv7/Qx6SlpRXaPy0trcTqvBk4Mxa/NmbMGEVERBR486N4nBmLL774QvPmzVNycnIpVHjzcGYsjh49qvXr1ysuLk4ffvihDh8+rEGDBiknJ0eTJk0qjbIrJGfGom/fvvrhhx/UoUMHGWOUm5urp59+Wn/5y19Ko2T8QlGf3ZmZmfrpp5/k5+d3Xfup8GeMUHFMnTpVS5cu1YoVK+Tr6+vucm4qFy5cUL9+/fT222+rRo0a7i7nppefn6+QkBC99dZbat26tXr37q3x48crMTHR3aXddDZu3KgpU6bozTff1M6dO7V8+XKtXr1aL774ortLg5Mq/BmjGjVqqFKlSkpPT3doT09PV1hYWKGPCQsLK1Z/XB9nxuKqV155RVOnTtWnn36q5s2bl2SZN4XijsWRI0d07Ngxde/e3d6Wn58vSfLy8tKBAwdUt27dki26gnLm30V4eLi8vb1VqVIle1vjxo2VlpamK1euyMfHp0RrrqicGYsJEyaoX79++vOf/yxJatasmS5evKiBAwdq/Pjx8vTk/ENpKeqzOyAg4LrPFkk3wRkjHx8ftW7dWuvWrbO35efna926dYqOji70MdHR0Q79JWnt2rVF9sf1cWYsJGn69Ol68cUXtWbNGrVp06Y0Sq3wijsWjRo10tdff63k5GT7cv/996tTp05KTk5WZGRkaZZfoTjz76J9+/Y6fPiwPZxK0sGDBxUeHk4ougHOjMWlS5cKhJ+rgdVwK9JS5bLP7uLNCy+fli5damw2m1mwYIHZu3evGThwoAkKCjJpaWnGGGP69etnxo4da++/efNm4+XlZV555RWzb98+M2nSJC7Xd5HijsXUqVONj4+P+X//7/+Z1NRU+3LhwgV3PYUKo7hj8WtcleY6xR2LEydOGH9/fzNkyBBz4MABs2rVKhMSEmImT57srqdQYRR3LCZNmmT8/f3Nv//9b3P06FHzySefmLp165pHHnnEXU+hwrhw4YLZtWuX2bVrl5FkZsyYYXbt2mWOHz9ujDFm7Nixpl+/fvb+Vy/Xf/75582+ffvMnDlzuFz/WmbPnm1q1aplfHx8zJ133mm++uor+7a7777b9O/f36H/u+++axo0aGB8fHxMkyZNzOrVq0u54oqrOGNRu3ZtI6nAMmnSpNIvvAIq7r+LXyIYuVZxx+LLL780bdu2NTabzdx6663mpZdeMrm5uaVcdcVUnLHIyckxf/3rX03dunWNr6+viYyMNIMGDTLnzp0r/cIrmA0bNhT63/+rr3///v3N3XffXeAxLVq0MD4+PubWW2818+fPL/ZxPYzhXB8AAIB0E8wxAgAAuF4EIwAAAAvBCAAAwEIwAgAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAlJgFCxYoKCjI3WUUKSoqSjNnznR3GS61efNmNWvWTN7e3urZs2ehbRs3bpSHh4fOnz9/Xfvs2LGjhg0bVmI1A2UJwQhwkS1btqhSpUrq1q2bu0spNRs2bFDXrl1VvXp1Va5cWbfddptGjhyp7777rkSO98QTT9g/7F1h+/btGjhwoMv2dy27du3Sww8/rNDQUPn6+qp+/foaMGCADh486NLjjBgxQi1atFBKSooWLFhQaFu7du2UmpqqwMDA69rn8uXL9eKLL7q0TlePJeAqBCPARebNm6dnn31WmzZt0unTp0v0WMYY5ebmlugxfss//vEPxcTEKCwsTP/973+1d+9eJSYmKiMjQ6+++qpba/stV65ckSTdcsstqly5cokfb9WqVfr973+v7OxsLV68WPv27dO//vUvBQYGasKECS491pEjR3TPPfeoZs2a9rN1v27z8fFRWFiYPDw8rmufwcHB8vf3d2mdQJl1g/d4A2B+vgt01apVzf79+03v3r3NSy+9ZN/26KOPFrjT9pUrV0z16tXNwoULjTHG5OXlmSlTppioqCjj6+trmjdvbpYtW2bvf/Vmih9++KFp1aqV8fb2Nhs2bDCHDx82999/vwkJCTFVqlQxbdq0MWvXrnU41unTp03Xrl2Nr6+viYqKMosXLza1a9c2r732mr3PuXPnTHx8vKlRo4bx9/c3nTp1MsnJyUU+35MnTxofHx8zbNiwQrdfvYHm/PnzTWBgoL29sBvPDh061OFGkMuWLTNNmzY1vr6+Jjg42HTu3NlkZWWZSZMmFbiZ5IYNG4wxP99t/uGHHzaBgYGmWrVq5v777zcpKSkFjjt58mQTHh5uoqKijDGmwOsgybz99tumZ8+exs/Pz9SrV8+89957DvW+9957pl69esZms5mOHTuaBQsWGElF3jT04sWLpkaNGqZnz57XfK2MMWbjxo3mjjvuMD4+PiYsLMyMGTPG5OTk2Ldf632SkpJS4PWZP39+oW1X30+/PPYXX3xh7r77buPn52eCgoLMvffea86ePWuM+fnGqUOHDrX3vXz5shk5cqSJiIgwlStXNnfeead9LIz5v3Ffs2aNadSokalSpYqJjY01p0+fNsaYa44l4G4EI8AF5s2bZ9q0aWOMMeaDDz4wdevWNfn5+cYYY1atWmX8/PzMhQsX7P0/+OAD4+fnZzIzM40xxkyePNk0atTIrFmzxhw5csTMnz/f2Gw2s3HjRmPM/wWj5s2bm08++cQcPnzY/PjjjyY5OdkkJiaar7/+2hw8eNC88MILxtfX1xw/ftx+rJiYGNOiRQvz1VdfmaSkJPuH3y8DQUxMjOnevbvZvn27OXjwoBk5cqSpXr26+fHHHwt9vjNmzDCS7B90RSluMDp9+rTx8vIyM2bMMCkpKWbPnj1mzpw55sKFC+bChQvmkUceMV26dDGpqakmNTXVZGdnmytXrpjGjRubP/3pT2bPnj1m7969pm/fvqZhw4YmOzvbftyqVauafv36mW+++cZ88803xpjCg1HNmjXNkiVLzKFDh8xzzz1nqlatan8djh49ary9vc2oUaPM/v37zb///W/zu9/97prBaPny5UaS+fLLL6/5Wp06dcpUrlzZDBo0yOzbt8+sWLHC1KhRw0yaNMne51rvk9zcXJOammoCAgLMzJkzTWpqqsnKyirQdunSpQLBaNeuXcZms5lnnnnGJCcnm2+++cbMnj3bfP/998aYgsHoz3/+s2nXrp3ZtGmTOXz4sHn55ZeNzWYzBw8etI+7t7e3iYmJMdu3bzdJSUmmcePGpm/fvsYYU+RYAmUBwQhwgXbt2pmZM2caY4zJyckxNWrUsP8f8NX1RYsW2fs/+uijpnfv3saYn//vu3LlygU+OOPj482jjz5qjPm/YLRy5crfrKVJkyZm9uzZxhhj9u3bZySZ7du327cfOnTISLIHgs8//9wEBASYy5cvO+ynbt265h//+Eehx3jmmWdMQEDAb9ZS3GCUlJRkJJljx44Vur/CHv+///u/pmHDhvYgaowx2dnZxs/Pz3z88cf2x4WGhhb48C0sGL3wwgv29aysLCPJfPTRR8YYY8aMGWOaNm3qsI/x48dfMxhNmzbNSLKffSnKX/7ylwLPY86cOaZq1aomLy/vut4nxhgTGBho5s+f79Dn122/DkaPPvqoad++fZG1/TIYHT9+3FSqVMl89913Dn06d+5sxo0bZ4wx9jNVhw8fdnguoaGh9vXCxhIoC7xK9os6oOI7cOCAtm3bphUrVkiSvLy81Lt3b82bN08dO3aUl5eXHnnkES1evFj9+vXTxYsX9d5772np0qWSpMOHD+vSpUv64x//6LDfK1euqGXLlg5tbdq0cVjPysrSX//6V61evVqpqanKzc3VTz/9pBMnTthr8/LyUqtWreyPqVevnqpVq2Zf3717t7KyslS9enWHff/00086cuRIoc/ZGHPd81OK4/bbb1fnzp3VrFkzxcbG6t5779VDDz3kUO+v7d69W4cPHy4wB+by5csO9Tdr1kw+Pj6/WUPz5s3tf1epUkUBAQE6c+aMpJ9fzzvuuMOh/5133nnN/RljfvOYkrRv3z5FR0c7vK7t27dXVlaWTp06pQsXLlz3+6S4kpOT9fDDD19X36+//lp5eXlq0KCBQ3t2drbDe6hy5cqqW7eufT08PNz+OgJlGcEIuEHz5s1Tbm6uIiIi7G3GGNlsNr3xxhsKDAxUXFyc7r77bp05c0Zr166Vn5+funTpIunncCNJq1ev1u9+9zuHfdtsNof1KlWqOKyPGjVKa9eu1SuvvKJ69erJz89PDz30kH1y8fXIyspSeHi4Nm7cWGBbUZfaN2jQQBkZGUpNTVV4ePh1H8vT07NAUMjJybH/XalSJa1du1ZffvmlPvnkE82ePVvjx4/X1q1bVadOnSLrb926tRYvXlxg2y233GL/+9evXVG8vb0d1j08PJSfn39djy3M1QCxf/9+RUdHO72f4rxPisvPz69YdVSqVElJSUmqVKmSw7aqVava/y7sdbzekAi4E1elATcgNzdXixYt0quvvqrk5GT7snv3bkVEROjf//63JKldu3aKjIzUf/7zHy1evFgPP/yw/YPjtttuk81m04kTJ1SvXj2HJTIy8prH37x5s5544gk98MADatasmcLCwnTs2DH79oYNGyo3N1e7du2ytx0+fFjnzp2zr7dq1UppaWny8vIqcPwaNWoUetyHHnpIPj4+mj59eqHbi/p9nFtuuUWpqakObcnJyQ7rHh4eat++vf72t79p165d8vHxsZ+N8/HxUV5enkP/Vq1a6dChQwoJCSlQ//Vejn69GjZsqB07dji0bd++/ZqPuffee1WjRo3ffK0aN26sLVu2OISHzZs3y9/fXzVr1ryh98lvad68udatW3ddfVu2bKm8vDydOXOmQB1hYWHXfczCxhIoCwhGwA1YtWqVzp07p/j4eDVt2tRh6dWrl+bNm2fv27dvXyUmJmrt2rWKi4uzt/v7+2vUqFEaPny4Fi5cqCNHjmjnzp2aPXu2Fi5ceM3j169fX8uXL7eHsb59+zqc3WjUqJFiYmI0cOBAbdu2Tbt27dLAgQPl5+dn/8omJiZG0dHR6tmzpz755BMdO3ZMX375pcaPH18gBFwVGRmp1157Ta+//rri4+P12Wef6fjx49q8ebOeeuqpIn/z5p577tGOHTu0aNEiHTp0SJMmTdI333xj375161ZNmTJFO3bs0IkTJ7R8+XJ9//33aty4saSff5Bxz549OnDggH744Qfl5OQoLi5ONWrUUI8ePfT5558rJSVFGzdu1HPPPadTp079xggWz1NPPaX9+/drzJgxOnjwoN599137bwUV9dVilSpV9M9//lOrV6/W/fffr08//VTHjh3Tjh07NHr0aD399NOSpEGDBunkyZN69tlntX//fr333nuaNGmSRowYIU9Pzxt6n/yWcePGafv27Ro0aJD27Nmj/fv3a+7cufrhhx8K9G3QoIHi4uL0+OOPa/ny5UpJSdG2bduUkJCg1atXX/cxCxtLoExw5wQnoLz7n//5H9O1a9dCt23dutVIMrt37zbGGLN3714jydSuXdthgq0xxuTn55uZM2eahg0bGm9vb3PLLbeY2NhY89lnnxljCk6WvSolJcV06tTJ+Pn5mcjISPPGG28UuILo9OnT5r777jM2m83Url3bLFmyxISEhJjExER7n8zMTPPss8+aiIgI4+3tbSIjI01cXJw5ceLENZ//2rVrTWxsrKlWrZrx9fU1jRo1MqNGjbJfrfbrydfGGDNx4kQTGhpqAgMDzfDhw82QIUPsk6/37t1rYmNjzS233GJsNptp0KCBfSK5McacOXPG/PGPfzRVq1Z1uMQ7NTXVPP7446ZGjRrGZrOZW2+91QwYMMBkZGQYY4qe6FvY5OsVK1Y49Pn1xOVfX64/d+5cI8n89NNP13yttm/fbh588EH7c6tXr54ZOHCgOXTokL3Pb12u/1vvk8LqLaytsPfTxo0bTbt27YzNZjNBQUEmNjbWvv3X76krV66YiRMnmqioKOPt7W3Cw8PNAw88YPbs2WOMKXzcV6xYYX75kVPUWALu5mEMX/oCN5NTp04pMjJSn376qTp37uzucsq9l156SYmJiTp58qS7SwHgAky+Biq49evXKysrS82aNVNqaqpGjx6tqKgo/eEPf3B3aeXSm2++qTvuuEPVq1fX5s2b9fLLL2vIkCHuLguAixCMgAouJydHf/nLX3T06FH5+/urXbt2Wrx4cYGrhnB9Dh06pMmTJ+vs2bOqVauWRo4cqXHjxrm7LAAuwldpAAAAFq5KAwAAsBCMAAAALAQjAAAAC8EIAADAQjACAACwEIwAAAAsBCMAAAALwQgAAMDy/wGaZIQHoksbxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: where is the actual average clustering coefficient of the dolphins network in the distribution? Can you show it?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems like the actual network has a much higher average clustering coefficient than the ER random graphs. This makes sense because the dolphins network is a social network, and social networks tend to have a high average clustering coefficient. \n",
    "\n",
    "But, let's think about the ER random graph model. Is the model a good null model for the dolphins network? Or, can it possibly be _too random_? Which of the important network properties does the ER random graph model not capture?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration model\n",
    "\n",
    "One very important property that the ER random graph model does not capture is, of course, the *degree distribution*. The degree distribution of the ER random graph is binomial (Poisson), which is very different from the degree distribution of the real networks. Let's check how different the degree distribution of the dolphins network is from the binomial distribution.\n",
    "\n",
    "Before doing this, here's a useful plot for showing the degree distribution of a small network. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Say, there are 4 nodes with degree 1, 6 nodes with degree 2, etc. \n",
    "degree_counts = {1: 4, 2: 6, 3: 6, 4: 5, 5: 4, 6: 3, 7: 2, 8: 1, 9: 1, 12: 1}\n",
    "plt.ylim(0, 7)\n",
    "plt.xticks(range(1, 13))\n",
    "for d, c in degree_counts.items():\n",
    "    plt.plot([d, d], [0, c], 'k-')  # the pin/stem \n",
    "    plt.plot(d, c, 'ko')  # the \"head\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: can you plot the degree distribution of the dolphins network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate an instance of ER random graph with the same number of nodes and edges, and plot the degree distribution of the ER random graph.\n",
    "\n",
    "**Q: can you plot the degree distribution of the ER random graph with the actual degree distribution of the dolphins network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You can use offset to make the dots and lines distinguishable.\n",
    "\n",
    "# offset = 0.1\n",
    "# ...\n",
    "# \n",
    "# for d, c in degree_counts.items():\n",
    "#     plt.plot([d+offset, d+offset], [0, c], 'k-')\n",
    "#     plt.plot(d+offset, c, 'ko')\n",
    "#\n",
    "# for d, c in er_degree_counts.items():\n",
    "#     plt.plot([d-offset, d-offset], [0, c], 'r-')\n",
    "#     plt.plot(d-offset, c, 'ro')\n",
    "\n",
    "\n",
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the difference is not dramatic, it seems to hint that the actual dolphin network may have slightly more nodes with high degrees than what we would expect from the ER random graph model. Let's check another network. This time, the famous karate club network. \n",
    "\n",
    "**Q: create the same plots for the karate club network.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# YOUR SOLUTION HERE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The discrepancy is even more dramatic in the karate club network. \n",
    "\n",
    "The thing is that the degree distribution is a very important property of the network. Degree distribution has an outsized impact on many network properties. For example, having hubs can dramatically decrease the average distance between nodes. Clustering coefficient is also affected by the degree distribution.\n",
    "\n",
    "In other words, the ER random graph model may be \"too random\" because it does not capture the degree distribution of the real networks. Is there any way to generate random graphs that exactly capture the degree distribution of the real networks, while keeping everything else random?\n",
    "\n",
    "That's where the \"configuration model\" comes in. The configuration model generates random graphs that have the same degree distribution as the real network. In other words, it keeps the degree sequence of the real network, in addition to the number of nodes and edges. You can picture the configuration model as follows:\n",
    "\n",
    "1. Break all the existing edges in the real network while keeping the \"ends\" of the edges (edge stubs) intact. If a node has degree $k$, then it keeps $k$ edge stubs.\n",
    "2. Randomly pick a pair of edge stubs and connect them. \n",
    "3. Repeat step 2 until all edge stubs are exhausted.\n",
    "\n",
    "Easy enough, right? Why don't you try a very small example by hand? You may encounter a few tricky cases. Did you find them? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details class=\"warning-class\"><summary>Solution</summary><p>Here's the tricky part. What if you are only left with two edge stubs connected to a single node? Should you connect them to create a self-loop? Or, should you just leave them unconnected? Or, what if two edge stubs that you pick are connected to the two nodes that are already connected? Should you connect them to create multiple edges between two nodes? Or should you find another pair? \n",
    "\n",
    "Another issue is that this process may generate a network with multiple components, even if the original network is connected. You want to be careful not to assume that the generated network is connected!\n",
    "</p></details>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create an instance of the configuration model using `nx.configuration_model` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple edges: {(17, 29), (14, 20), (32, 33), (42, 47), (41, 45), (36, 51), (6, 51), (9, 33), (8, 15)}\n",
      "Self-loops: {16, 33}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_dolphin_net = nx.configuration_model([d for n, d in dolphin_net.degree()])\n",
    "\n",
    "# check multiple edges\n",
    "multiedges = set()\n",
    "for n1, n2 in random_dolphin_net.edges():\n",
    "    if random_dolphin_net.number_of_edges(n1, n2) > 1:\n",
    "        multiedges.add((n1, n2))\n",
    "print(\"Multiple edges:\", multiedges)\n",
    "\n",
    "# self loops\n",
    "selfloops = set()\n",
    "for n in random_dolphin_net.nodes():\n",
    "    if random_dolphin_net.has_edge(n, n):\n",
    "        selfloops.add(n)\n",
    "print(\"Self-loops:\", selfloops)\n",
    "\n",
    "nx.draw(random_dolphin_net, node_size=30, width=0.5, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you repeatedly run this code, you can probably see that the multiedges and self-loops are almost always present, and the network is sometimes disconnected. \n",
    "\n",
    "Always be aware of these issues! Don't just assume that the network generated from the configuration model is connected and has no multiedges or self-loops, all of which make it tricky to compute many network properties. \n",
    "\n",
    "You may be wondering, \"why don't we just rewire the multi-edges and self-loops until we don't have any?\" That's a good question! In fact, that's what people do to make the network more \"clean\". This can be done by 'edge swapping', a process that swaps a pair of edges so that all the degrees are preserved. We can keep doing this until we don't have any multi-edges or self-loops. The problem is that this process makes the network properties biased! So this is not perfect. To make the network truly random and unbiased, we need to allow multi-edges and self-loops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's do some comparisons!\n",
    "\n",
    "Let's try the clustering coefficient comparison that we did above with two networks (dolphins and karate club) and two null models (ER and configuration model). For multi-edges and self-loops, let's do a quick-and-dirty thing: just remove them.\n",
    "\n",
    "You can do the following to remove them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multiple edges: set()\n",
      "Self-loops: set()\n"
     ]
    }
   ],
   "source": [
    "g = nx.Graph(random_dolphin_net)\n",
    "g.remove_edges_from(nx.selfloop_edges(g))\n",
    "\n",
    "# check multiple edges\n",
    "multiedges = set()\n",
    "for n1, n2 in g.edges():\n",
    "    if g.number_of_edges(n1, n2) > 1:\n",
    "        multiedges.add((n1, n2))\n",
    "print(\"Multiple edges:\", multiedges)\n",
    "\n",
    "# self loops\n",
    "selfloops = set()\n",
    "for n in g.nodes():\n",
    "    if g.has_edge(n, n):\n",
    "        selfloops.add(n)\n",
    "print(\"Self-loops:\", selfloops)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Q: let's compare the average clustering coefficient of the real networks with the average clustering coefficient of the ER random graph and the configuration model.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dolphins\n",
    "\n",
    "# YOUR SOLUTION HERE\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# karate club\n",
    "\n",
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! For the dolphin network, both random graph models produce very similar distributions of the average clustering coefficient, which is much smaller than the actual average clustering coefficient of the dolphin network. However, for the karate club network, the two distributions are quite different. According to the ER random graph model, the average clustering coefficient of the karate club network is much higher than what we would expect. On the other hand, if we consider the configuration model, the average clustering coefficient of the karate club network is not at all surprising, but it is what's expected from the degree distribution! \n",
    "\n",
    "This difference is likely due to the fact that the Karate Club network has several hubs with large degrees, which is not captured by the ER random graph model. \n",
    "\n",
    "Let's try another comparison, this time with a statistical test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing assortativity \n",
    "\n",
    "Lets try another mock hypothesis using the [dolphin social network](http://www-personal.umich.edu/~mejn/netdata/dolphins.zip). My hypothesis is that dolphins have a posh and very exclusive culture. Popular dolphins only hang with other popular dolphins, while the loners are stuck to mingle among themselves. A sad state of affairs, but social life is hard in the pods. How might we test this?\n",
    "\n",
    "There happens to be a similarity measure called [assortativity](https://en.wikipedia.org/wiki/Assortativity) where nodes of a certain type tend to be connected to nodes of the same type. In networkx there is a function called [degree assortativity](https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.assortativity.degree_assortativity_coefficient.html?highlight=degree_assortativity_coefficient#networkx.algorithms.assortativity.degree_assortativity_coefficient) which tells us how strongly nodes of similar degree are attached to each other. If the network has high degree assortativity, then low degree nodes will be connected to other low degree nodes, and high degree nodes will be connected to other high degree nodes. Conversely, low degree assortativity (or even disassortativity) would imply no (or negative) relationship. \n",
    "\n",
    "Excellent, so we have a measure, but what do we compare our graph too? It wouldn't really be appropriate to compare it to an ER graph because the nodes all have different degrees which are about the same and normally distributed. Instead, we want to compare our dolphin network to a graph with the same degree distribution, and that is where the configuration model comes in."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to do a rough test for our hypothesis by calculating the z-score for our dolphin network's degree assortativity coefficient. We will do this by generating a bunch of configuration models based on the dolphin degree sequence and calculating the assortativity coefficient for those null graphs. We can then compare our real network with the null model. If our z-score is high then it is unlikely that the dolphin network's assortativity can be accounted for by just the degree-sequence of a random graph, which means something more interesting is at work.\n",
    "\n",
    "Lets carry out this experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "dolphin_net = nx.read_gml(\"dolphins.gml\")\n",
    "dolphin_net = nx.Graph(dolphin_net)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the graph is loaded in lets calculate the degree assortativity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.04359402821531255\n"
     ]
    }
   ],
   "source": [
    "real_assortativity = nx.degree_assortativity_coefficient(dolphin_net)\n",
    "print(real_assortativity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting! So it appears that the network is disassortative, so it seems like our hypothesis about the dolphins is falling appart, but we still need to compare it to a null model in order to determine whether there is anything of interest to be pursued here.\n",
    "\n",
    "**Q: can you calculate the degree assortativity of the configuration model and compare it to the degree assortativity of the dolphin network?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# YOUR SOLUTION HERE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can calculate the z-scores using scipy's [zscore](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.zscore.html) function. It will print out the z-scores for all the elements of the distribution. If we assume that the dolphin network came from the null distribution then we can calculate the z-score for the dolphin network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.006337357506093644\n"
     ]
    }
   ],
   "source": [
    "# Prepend the real assortativity to the model list and have scipy calculate the zscores\n",
    "zscores = stats.zscore([real_assortativity] + model_assortativity)\n",
    "\n",
    "# Just print out the first score which corresponds to the real network\n",
    "print(zscores[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A z-score corresponds to how many standard deviations out from the mean the sample is. A score of +1 would be one standard deviation above the mean. Since the score is close to zero we can safely conclude that the disassortativity we observed in the dolphin network is explainable entirely from the degree sequence and no other special properties of the network.\n",
    "\n",
    "However, this doesn't mean there aren't low-level features hidden in the network. The degree assortativity score is an aggregative measure, and it can obfuscate local deviations from assortativity that are non-random. There is also a score for the local degree assortativity, but we will not pursue that further here.\n",
    "\n",
    "Now that we have a distribution of null assortativities we can also visualize the distribution and plot our dolphin network's assortativity along with it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model_assortativity, bins=50)\n",
    "plt.axvline(real_assortativity, lw=1, color=\"red\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And there we have it! We have fair reason to conclude that not only is the hypothesis wrong but the surprising disassortativity we found really wasn't anything special after all. [note: reference for [`axvline`](http://matplotlib.org/api/pyplot_api.html?highlight=axvline#matplotlib.pyplot.axvline) in matplotlib]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now try it yourself!\n",
    "\n",
    "1. Pick a graph of your choosing. You can use the graphs you are using for your project or any other real-world graph. Here are a couple places you can find some: [pajek datasets](http://vlado.fmf.uni-lj.si/pub/networks/data/), [Newman's datasets](http://www-personal.umich.edu/~mejn/netdata/). [note: if you use directed graphs makes sure you use the corresponding function [`directed_configuration_model`](https://networkx.github.io/documentation/stable/reference/generated/networkx.generators.degree_seq.directed_configuration_model.html?highlight=configuration_model#networkx.generators.degree_seq.directed_configuration_model)]\n",
    "\n",
    "2. Construct two simple hypotheses around two different measures that you can calculate from the graph. Here is a [list of algorithms that networkx has](https://networkx.github.io/documentation/stable/reference/algorithms/index.html). You aren't limited by measures networkx can calculate. However, do not use a measure that depends entirely on the degree sequence. For instance, it would be silly to compare average degree to the random networks generated by the configuration model because it uses the same degree sequence and so will have the same average degree.\n",
    "\n",
    "3. Carry out a simple hypothesis test for both hypotheses. It can be similar to what I showed here. You are welcome to carry it out in a more robust manner, but the focus of this assignment is getting a hang of using configuration models as null models. Here is a link to [Statistics for Hackers](https://speakerd.s3.amazonaws.com/presentations/7e68b43159d646cf81eda9e1bded8213/Statistics_for_Hackers_-_PyCon2016.pdf). It has a nice little python tutorial for how you can carry out hypothesis testing without all the hard math. Even if you don't use it for this assignment I highly recommend checking it out.\n",
    "\n",
    "4. Provide quantitative analysis and a graphical illustration of your results. It should be clear that your hypothesis was validated/invalidated/inconclusive.\n",
    "\n",
    "5. Answer the following questions:\n",
    "  * What graph are you using?\n",
    "  * What are you hypotheses?\n",
    "  * What measures will you be using to test your hypotheses? How do these measures accomplish this?\n",
    "  * Explain your results. Were they surprising? Did they confirm or reject your hypotheses?\n",
    "  * From these tests, what have you learned about the structure of the network you were investigating?\n",
    "\n",
    "6. Once you are complete submit your Jupyter notebook to Canvas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
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}