4_Mixing_Patterns.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**4th European Conference on Social Networks**\n",
    "\n",
    "**Workshop: Introduction to Python's Graph-Tool**\n",
    "# graph-tool basics: investigating mixing dynamics in networks.\n",
    "\n",
    "**Author**: <a href='https://marcosoliveira.info/'>Marcos Oliveira</a>, GESIS - Leibniz Institute for the Social Sciences\n",
    "\n",
    "**Version**: 9 Sep 2019\n",
    "\n",
    "**Description**: Here we analyze mixing in networks in Python using the <code>graph-tool</code> library. \n",
    "\n",
    "**License**: <a href='https://www.gnu.org/licenses/gpl-3.0.en.html'>GNU General Public License 3.0</a>\n",
    "    \n",
    "## Imports and Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'retina'\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger\">\n",
    "    You need the <code>graph_tool</code> library in your machine. Sometimes the installation is challenging. This <a href=https://git.skewed.de/count0/graph-tool/wikis/installation-instructions>page</a> might help you.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.28 (commit e5760dfc, Thu Jun 6 15:14:05 2019 +0100)\n"
     ]
    }
   ],
   "source": [
    "import graph_tool.all as gt\n",
    "print(gt.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-warning\">\n",
    "    If you running this notebook via the GESIS infrastructure, you should set the number of threads as the following; otherwise, it will be slow.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gt.openmp_set_num_threads(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def node_colors(g, group_memberships):\n",
    "    \"\"\"\n",
    "        Given a graph and a group membership, this function returns \n",
    "        a vertex property with node color for each group.\n",
    "    \"\"\"\n",
    "    colors = ['#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6']\n",
    "    to_rgb = lambda h: list(int(h[i:i+2], 16)/255. for i in (0, 2, 4))\n",
    "    vertex_color = g.new_vertex_property('vector<double>')\n",
    "    for m, v in zip(group_memberships, g.vertices()):\n",
    "        vertex_color[g.vertex(v)] = tuple(to_rgb(colors[m % len(colors)][1:]) + [255.0])\n",
    "    return vertex_color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mixing patterns\n",
    "Here we are going to study the mixing patterns of a given network using ```graph-tool```.  \n",
    "\n",
    "## Network\n",
    "Let's load a network from the ```graph-tool``` library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = gt.collection.data[\"polblogs\"].copy()  # polbooks, polblogs, football"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = gt.GraphView(g, vfilt=gt.label_largest_component(g))\n",
    "g = gt.Graph(g, prune=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph contains also metadata about the nodes in the vertex property ```value```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VertexPropertyMap object with value type 'double', for Graph 0x14a78d390, at 0x14a78dd90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.vertex_properties['value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 10 first values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[g.vertex_properties['value'][v] for v in g.vertices()][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The unique values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 1.0]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_values = list(set([g.vertex_properties['value'][v] for v in g.vertices()]))\n",
    "unique_values.sort()\n",
    "unique_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0.0: 0, 1.0: 1}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_types_dict = dict(zip(unique_values, range(len(unique_values))))\n",
    "node_types_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_types = g.new_vertex_property(\"int\") \n",
    "for v in g.vertices():\n",
    "    node_types[v] = node_types_dict[g.vertex_properties['value'][v]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's color the nodes with this information. For this, we use the ```node_colors``` helper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = node_colors(g, node_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gt.graph_draw(\n",
    "    g, \n",
    "    vertex_color=colors, vertex_fill_color=colors,\n",
    "    vertex_size=15, vertex_shape=\"square\",\n",
    "    edge_pen_width=1.1, output_size=(600, 600));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like we have some kind of meso-level structure here: nodes of the same type tend to connect among themselves. We can measure this via Newman's assortativity, as the following (<a href='https://graph-tool.skewed.de/static/doc/correlations.html#graph_tool.correlations.assortativity'>documentation</a>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.84238031923605, 0.004283367956768865)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gt.assortativity(g, node_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Newman's assortativity goes from -1 to 1, from a dissortative to an assortative network. In the case of zero, there is not evidence for assorativity/dissortativity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data mixing matrix\n",
    "\n",
    "We can analyze the number of edges inter- and intra-group to investigate the mixing in the network. In fact, Newman's assortativity is a measurment of this matrix.\n",
    "\n",
    "To retrieve the mixing matrix of a network, we use the function <code>gt.<b>corr_hist()</b></code>.\n",
    "\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "The signature of the <code>corr_hist</code> follows:\n",
    "    \n",
    "- graph_tool.correlations.<b>corr_hist</b>(g, deg_source, deg_target, bins=[[0, 1], [0, 1]], weight=None, float_count=True)\n",
    "\n",
    "For now, let's focus on the <code>deg_source</code> and <code>deg_target</code> parameters (see the <a href=\"https://graph-tool.skewed.de/static/doc/correlations.html#graph_tool.correlations.corr_hist\">documentation</a> for more). \n",
    "\n",
    " - The <code>deg_source</code> and <code>deg_target</code> parameters are the vertex properties. You can also use `in`, `out`, or `total`, if you want to use the degree property of the vertices.\n",
    "</div>\n",
    "\n",
    "Here we use the ```node_types```, which gives us the type of each node (i.e., meta data). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VertexPropertyMap object with value type 'int32_t', for Graph 0x14a78d390, at 0x117cbf850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_mixing_matrix, bins = gt.corr_hist(g, node_types, node_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7015.,  535.],\n",
       "       [ 712., 7579.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mixing_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 251
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(data_mixing_matrix);\n",
    "plt.xticks(range(data_mixing_matrix.shape[0]), bins[0]);\n",
    "plt.yticks(range(data_mixing_matrix.shape[1]), bins[1]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 7015.0\n",
      "0 1 535.0\n",
      "1 0 712.0\n",
      "1 1 7579.0\n"
     ]
    }
   ],
   "source": [
    "for (i, j), z in np.ndenumerate(data_mixing_matrix):\n",
    "    print(i, j, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 251
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(data_mixing_matrix);\n",
    "plt.xticks(range(data_mixing_matrix.shape[0]), bins[0])\n",
    "plt.yticks(range(data_mixing_matrix.shape[1]), bins[1])\n",
    "for (i, j), z in np.ndenumerate(data_mixing_matrix):\n",
    "    plt.text(j, i, '%d' % z, ha='center', va='center', \n",
    "             color=\"white\" if z < data_mixing_matrix.mean() else \"black\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's include the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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njz1sdyjPT1P+2jjpsKmpKVuO33yN57v/1um589q709TUlMNPWPsdu1NuaLzf22qCC5ltCJ6Xp3DXdV1M1M69CZN6eXU2Gr89r3Hp15Gjm5ZdHKmkqqqM27ElU25vy7RVfHLhgfsa08av4hMQQO97+tHGd59sbUP/vNcTexKWvsMf1c30pfev7gBvT41DH+joqPP7CxsXPTr89UOXHevvzosPaLxbv+GarteOT5JFrXVuvaFxfYR9DhzUg2u6en+7Zfl1Gbba5nnZ0ZBd99tx2c9PTH9qreZduudh4bzy63eVy913pySusbCh6IlImNp52925Ajt13nZ3rkFPj0MfuOHa1mWHB1Z1qGGpw49sPGb6/e255g9dv6jpwp/Py7y5dQYNrvKKw7tekXFd1XW9yunz5i7JD77ZuFb9bnsOzCZje/dQB/SGoSOG5K3/8oYkjesdzH56zrJpTc2r3izs/tJdsuv+jT/Hd1xb/lREd15y+F7Z4+UvTJL89Xc3r9W89I2eiISrOm8nV1X1rPGqqhqR5KAkC5Jcv5pxrk+yMMlBnfOtOE5TGh+zXHF59CNLT1gcv1NLXrjn6t/577z7wLzytY2N/1mnzsifr2qEQkdHnYvPn5evfb6xw+jY93T93oYkaVtUZ9bMjmX/tbY2Nv7tHXnW/fPmPPvEyL8/0pF3H/14LvrFvDz+6PLzDRa31fnLHxfmvW96Ig9Na09TU3LSvzj9hQ3TFttvlnOvOztHvPvQbLbtpsvuH9AyIC85fK985drPZNudt05Hx5Iu39Pwb788JSd85tjsuPf4NA9Y/tobvdnIvOFDr8ln/vdf09TUlCcfejqXfu+Pz5r3Hz5+dE75zvsz6ZV7ZMjw5R8fHrXpyLzlo6/Pp84/NUny0N2P5tLvXrkenjk97TnvS63r+v6qqi5LYyP+gSRfXWHymUmGJfnvFa+RUFXVLp3z3r3COPOqqvpRGtdVOCPJKSuM88Ek45Jc6hoJ/c+8uUvyp8tWfW2EktM+PyaPPNSeu+9oy8knPJXBQ6os6ajT1vntsi89bHBOPLl89Omy38zPWR+d2eX+u+9oy+RJjy77fdJ+Xb/p8c5b23LnrY15Bw2qMnholfnzlqS98/SIwUOqfPzsMdnnQNdIYMO19Gudk2TRwsZFjYaOHJqWgY0/+wvnt+bc9387t15157PmG7HJsBx32jE57rRj0tHekfmzF6R5QHOGjVq+R++Rex7L6Ud9octXTbcMaskRJxyaI044NEuWLMmCOQtT13VGbDJ82WOm3f5gTj/qC1nc5qTgDUFPHXA9KY3LKZ9bVdVhSaYk2S+Naxrck+QTKz1+6T6qlQ9cn5bkFUk+UlXVXkluSLJrkqOSPJlGhNDPXHHxgixqrdPUlLz6DWt+aGD4iKZ85/wt8tPvzM1lv5mfRx5sT8ugKhN3a8mRbx6eo48dlqrq2S9yGrNZU045Y5PcduOi3DulLc/MXJJ5c5dkyJAq2+7akn0OHJw3vn24cxHYoD3zxOx87UPfye4H7ZoJe26f0ZuNzLBRQ9M6f1Gm3TY9t1x5Z/73vy7Nkw91verhtz76w+z3uhdnz5fvli3GbZbRm49KU1OVpx6ZkWm3PZg/X/jXXPHja4qXVr76vL+keUBzXnjAztl6hy0ycuyINLc0Z8ZjM3P/rdNz9fnX58qfXCMQNiDV6o7RrvFAVbVtkrPSuGzy2CR/T3JBkjPrun5mpcfWSVLXdZctQFVVY9K4UuPRSbZKMiPJJUlOr+v6kee4jjftsnvLpB/+dqvnMgywDk4bv29frwJslP5aX5G5mXVzd5/8W5Uee7tU1/XDaXwR05o8ttu3h3Vdz0zji6E+3EOrBgCsg967lB0AsEERCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUDejrFehtj/5tRD6568F9vRqw0bn0sev7ehVgo7TP5IW5+Y51m9eeBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQNGAvl4Bnr8uXfCjNX7sqZPPzh3X3r3s96qqssfLdsnEF0/IxEnjM/HFE7LluM2SJOd+6Hu5+H+uXOV4ux+0c/Z/zd7Z7cCJ2XanrTJkxODMe2ZBpt3xUK78xXW54ifXpq7rdXti0A80b3XfGj/2yvNfkJcfOGTZ79MfXpwd9n1wtfP99ZJt8pK9Bj/rvu//Yk7e889PrtFyx207IPffMK7L/YsX1/mfn87Jz86fmzuntmVRW51tthqQ1xw2NB/74CbZagubpv7C/wnWm5lPzFrl9KEjhmTw0EFpW7Q40+965NnTRg7JF39/2jot99iPvT7vOuPNy37vaO/IwnmtGb35yEw6bPdMOmz3HH78wTn9jV/Kgrmt67QM6GtbbNa8yulz5i7JwtY6Awcmu+8ycJ3GaWmputw3ZHC12mU/8VRHkmTvFw3qMm3W7I4c+Y6/57obG6+9gQOToUOact8Di3Pu/8zOj341N7/76dbZd+/BXeal94kE1ptjx/+/VU7/xvWfyQ57bJ8bLrk1c2fO6zJ94bzW3Hfr9Nxz8wO556ZpOfELb8vYLUevdrnNLc2ZM2NuLv/Jtbn6/L/mnpsfyJKOJRmxybAcddLkHPcvR+VFL90lJ3/jH3P2O762zs8P+tJjt49f5fRJr3wot/2tLa995bCMHdP9Rn1146zsrUeNyFuPGtHt9FvuWJSXTH44SfLOt47sMv09Jz+Z625szfBhVf7ri5vnzUcOT0tLlan3teV9H30yV1/fmqOO/3umXLtdRo9adYyw/okE+sSEPbbLDntsnyS5/CfXdJk+f/aCvGGLE591SOCEs96yRmNf95ub8utzL+myl2DuM/Pz47MvSF0nx3/ymBz8xv3y7dN+licfnvEcngn0P7feuSi3/a0tSXL8W7puqNenH/5yTpJk802b8+pDh3ZZrwsvmZ8k+fdPbZrjjlkeGzvvODAXfH+r7HTAg3ny6Y78xzdm5TP/Orb3VpwiJy7SJ171tpclSZ55YnZu+P1txces6zkD0+54aJWHES7/0dXLft5p0tq9i4INwYob6tccNnQ1j+45ixfX+dkFjb2Cxx0zPAMGPPtwxaVXLUiSjBhe5T3HdY2X0aOa867OvQ8//tVc5w31Az0SCVVVvamqqq9WVXVNVVVzqqqqq6r68TqOtU1VVd+tquqxqqoWVVU1vaqqr1RVtUlPrCt9r6m5KYe85YAkyVW//EuWdCzp1eXPWeHQRlOTTub5pb19+Yb62Dd03VCvT5dcuSBPzWicj1Dag/HgI4uTJDuMa0lzc3m9dt6xJUny8GPtmXrf4vW0pqypnjrc8MkkeyaZl+SRJLusyyBVVe2Q5Lokmye5KMndSfZN8uEkR1RVdVBd1/YNb+D2OXzPbLLFqCTlQw3r2x4vW/7Pc+UTJmFDd8mVC/Lk091vqFd20Osezt+mtmVxe7LlZs05cJ/B+afjR+Wl+w1Z7bwrW7oHY8/dBmbP3bqetFh1dkFHR/djtLcv//lvU9uyy07dn3TJ+tdTb6NOTjIxycgk738O43wjjUD4UF3XR9d1/fG6rg9N8uUkOyc5+zmvKX1u8tsbhxruv/3BTLv9oV5ddlVVeccnj0mS3PXXe/Pw1Md6dfmwvv3gF8s31Hvt3nVDvbLrb1qUpqbG1nv6w+356a/n5eVHP5qT/+2ptdrdP2NmRy6+onG+wTu7iZPtt2nsJbh/+uIsWlQee8q9bct+fvzJ9uJj6D09Egl1XV9V1/W99XM4gNS5F2FykulJvr7S5E8lmZ/kHVVVDVvnFaXPjdhkWPZ99V5Jkst/fG2vL/+dn3pjJk6akPbF7fnmR9fpiBj0WzOfWb6hPv7N3e9FGDyoyvvfNSp/vOAFmX3fhMycOiHzpk3IjZduk9dNbpzDcO7/zM7nz31mjZf98wvnpq0tGTAgOfaY4cXHvOrljbEXLKzzX9+f3WX6k0+3L4ucJJk7r3cPRdJVfzoge0jn7WV1XT/rX0Zd13OT/DnJ0CT79/aK0XNe8ZYDMnBQS9oXt+fKX1zXu8t+8/5566lHJkm+d/p5mfp/03p1+bC+rbihPu6N5Q11kmy5+YB87XOb5WX7D8nwYY3NQFVVmbTH4Fz0g63zpiMb837u3Gcya/Yqjg2s4Ie/nJskOeLQodl80/KR7L1fNCivfVUjFE773Iyc++1ZeerpjixaVOfqvyzMq499LK0r7GFYuoeDvtOfImHnztt7upl+b+ftxNUNVFXVTaX/so7nStBzXvW2lyZJbrzs9sx+as5qHt1z9j1iz5z67X9KU1NTLvj6pfnVf/6u15YNvWVNNtRr4vOfbHz0cP6COn+4ZuFqH3/X1Lb8322LknR/qGGp731li+yz16AsWlTn5NOfzpYveiBDx92fQ455NHdMacuXz9ps2WNHjexPm6iNU3+6TsKoztuu+6Ceff/qr6ZDv7Ttzltn55fskCS54se9d8LiXq94YT75kw+lZeCAXPrDPznMwPPSlHvacuOtjQ31qg41rInx27Vks7HNeWpGRx54aPWfMPjheY3gH7NJU173qlUfER47pjlXX7RNvvfzOfn1xfNy//TFGTCgyqQ9BuWfTxydzTddfgGlnSa0PKfnwXPXnyKhx9R1/eLS/Z17Eyb18urQaekJi3NmzM31v7ulV5a52wETc+Z5H8mgIQPzp19dn6+c9J1eWS70th/8cvmG+sjJvXfqVkdHnZ+c39iD8Q9Hj8jAgas/RDBwYJV/On5U/un4UV2m/frixsc3BwxIXrKnSzP3tf60L2fpnoKu/2qeff+qvxCAfqmpqcqhxx6YJPnjedenffGaHed8LnZ+yYR8+tenZPCwQfnLxTfnC+/+ZpYscXEWnn/WZUO9Kg88tHjZ9Q7Gbbfqd/OX/2lBHnu88dh3vqX7yzWvqZ9f2Hger33lsIwc0Z82URun/vR/YGrnbXfnHOzUedvdOQv0Y3sfuns23XpMkt65NsKEF22Xsy/8aIaNGpqbrrgjZ7/tq+loX/9hAn3hiquXb0YzSxYAAAW1SURBVKiPX4MN9eo+iPaJzzUuRzNkcJVDD1r19RJ+eF5jo/7CiQO7fGPk2rr0qvn59cXz09SUfOyDrp/XH/Snww1Xdd5OrqqqacVPOFRVNSLJQUkWJLm+L1aO52bpCYsP3vVI7rnpgTWaZ+jIIRnQsvz45NIznQcPHZSRY5efub1wbmsWty3/PPU2O22Zz/7mYxkxZnhuu3pKznjLl581HZ5vVtxQ77MGG+pDj3k0hx8yNK+bPCy77jQwzc1V6rrOrXe25dPnzMxFv298jPJjH9wkYzbp/kuWZs/pWPbYNYmTJDnvf+dlxsyOvPaVQ7PN1gNSVVWefLo93//53Jz1pZmp6+TUk0Zn/xc71NAf9HokVFXVkmSHJIvrur5/6f11Xd9fVdVlaVwr4QNJvrrCbGcmGZbkv+u6nt+b68tzN3TE4BxwZOM0kct/subXRjjjlydnz4N37XL/iZ8/Lid+/rhlv//Hid/K5SucCPmWU1637IqOE3bfNj+Yck63yzj/K5f4pAMbtDlzl+SiS9ZuQ/3gI+35xOdm5hOfm5mWlmTk8KYsWFhnYevyPQwffM+o/NtHVv1u/pe/mZfW1jrNzcnb37Rmy75r6qKc9aVn8oE0viZ6yOCmzJ7TeE9YVclH3jc6n/uEL3bqL3okEqqqOjrJ0Z2/btl5e0BVVd/v/Pnpuq5P7fz5BUmmJHkwybiVhjopjcsyn1tV1WGdj9svjWso3JPkEz2xvvSug9+4XwYPHZSOjiX5w8//vN6XV1XLj6KNGNP9Z8WTZPDw1V+RDvqzX/5mbha21mlqSt72xjXbUH/h9E1zxZ8W5MZbW/P4kx2ZOasjA1uq7LxDSw7cd3De+/ZR2W/S6t/J/6jzI5evPHhottpizTYnR04eliee6sh1N7bm4cfas7C1zrhtB+TgA4bkfe9cs+XSe6qe+JatqqrOSOOqiN15sK7rcZ2PHZfkgRXvW2msbZOcleSIJGOT/D3JBUnOrOt6zS//VV7Pm0ZUYyYdMPg1z2UYYB1cMs2RQugL+0x+ODffsejm7j75tyo9siehruszkpyxho+dnqTbU2/run44yQk9sV4AwLrrT59uAAD6EZEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFIkEAKBIJAAARSIBACgSCQBAkUgAAIpEAgBQJBIAgCKRAAAUiQQAoEgkAABFIgEAKBIJAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQFFV13Vfr0OvqapqRlOaxwyrRvX1qsBGZ6fd5/f1KsBGacq9bVnYWs+s63rs2s67sUXCA0lGJpnex6vCutml8/buPl0L2Ph47W3YxiWZU9f1+LWdcaOKBDZsVVXdlCR1Xb+4r9cFNiZeexsv5yQAAEUiAQAoEgkAQJFIAACKRAIAUOTTDQBAkT0JAECRSAAAikQCAFAkEgCAIpEAABSJBACgSCQAAEUiAQAoEgkAQJFIAACKRAIAUCQSAIAikQAAFP1/u1imZypWMI0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 260
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(data_mixing_matrix);\n",
    "plt.xticks(range(data_mixing_matrix.shape[0]), [unique_values[l] for l in bins[0][:len(unique_values)]])\n",
    "plt.yticks(range(data_mixing_matrix.shape[1]), [unique_values[l] for l in bins[0][:len(unique_values)]])\n",
    "for (i, j), z in np.ndenumerate(data_mixing_matrix):\n",
    "    plt.text(j, i, '%d' % z, ha='center', va='center', \n",
    "             color=\"white\" if z < data_mixing_matrix.mean() else \"black\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can analyze further this matrix by comparing it against a random version of our network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generation\n",
    "\n",
    "The <a href=\"https://graph-tool.skewed.de/static/doc/generation.html\">generation module</a> of the ```graph-tool``` package enables us to generate networks using different models. Here we will use the <a href=\"https://en.wikipedia.org/wiki/Configuration_model\">configuration model</a> to examine the mixing dynamics in our data.\n",
    "\n",
    "## The Configuration Model\n",
    "Let's first create a copy of our network:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_model = g.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```graph-tool``` is a bit picky about graph properties, we need to recreate them for this copy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_node_types = null_model.new_vertex_property(\"int\") \n",
    "for v in null_model.vertices():\n",
    "    null_node_types[v] = node_types_dict[null_model.vertex_properties['value'][v]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create a random version of our network by rewire the edges of it while preserving its degree sequence. For this, we use the <code>gt.<b>random_rewire</b>()</code> function. \n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "The signature of the <code>random_rewire</code> follows:\n",
    "    \n",
    "- graph_tool.generation.<b>random_rewire</b>(g, model='configuration', n_iter=1, edge_sweep=True, parallel_edges=False, self_loops=False, configuration=True, edge_probs=None, block_membership=None, cache_probs=True, persist=False, pin=None, ret_fail=False, verbose=False)\n",
    "\n",
    "For now, let's focus on the <code>model</code> parameter (see the <a href=\"https://graph-tool.skewed.de/static/doc/generation.html#graph_tool.generation.random_rewire\">documentation</a> for more). \n",
    "\n",
    " - The <code>model</code> parameter determines how the edges are rewired. If we set <code>model='configuration'</code>, we use the configuration model in which the edges will be rewired but the degree sequence keeps the same.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt.random_rewire(null_model, model='configuration');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixing_matrix, bins = gt.corr_hist(null_model, null_node_types, null_node_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4020., 3530.],\n",
       "       [3707., 4584.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixing_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "What is the difference between this matrix and the mixing matrix from data?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use some heatmap again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 257,
       "width": 260
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(mixing_matrix);\n",
    "plt.xticks(range(mixing_matrix.shape[0]), [unique_values[l] for l in bins[0][:len(unique_values)]]);\n",
    "plt.yticks(range(mixing_matrix.shape[1]), [unique_values[l] for l in bins[1][:len(unique_values)]]);\n",
    "for (i, j), z in np.ndenumerate(mixing_matrix):\n",
    "    plt.text(j, i, '%d' % z, ha='center', va='center', \n",
    "             color=\"white\" if z < mixing_matrix.mean() else \"black\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing data to the networks generated by the configuration model\n",
    "The rewiring is stochastic. Now we can follow this procedure several times and compare against our data. \n",
    "\n",
    "We create a function for rewiring:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rewire_graph(g):\n",
    "    null_model = g.copy()\n",
    "    null_node_types = null_model.new_vertex_property(\"int\") \n",
    "    for v in null_model.vertices():\n",
    "        null_node_types[v] = node_types_dict[null_model.vertex_properties['value'][v]]\n",
    "    gt.random_rewire(null_model, model='configuration')\n",
    "    mixing_matrix, bins = gt.corr_hist(null_model, null_node_types, null_node_types)\n",
    "    return mixing_matrix, bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then call it several times, say $N=500$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mixing_matrices = []\n",
    "for _ in range(500):\n",
    "    mixing_matrix, bins = rewire_graph(g)\n",
    "    mixing_matrices.append(mixing_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see the number of intra-edges in the first group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([m[0, 0] for m in mixing_matrices], edgecolor='k', bins=20)\n",
    "plt.xlabel(\"$E_{00}$\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can compare this against our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 382
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([m[0, 0] for m in mixing_matrices], edgecolor='k', bins=20)\n",
    "plt.axvline(data_mixing_matrix[0, 0], color='red');\n",
    "plt.xlabel(\"$E_{00}$\")\n",
    "plt.ylabel(\"Frequency\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now have a z-scored mixing matrix from data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3987.366, 3562.634],\n",
       "       [3739.634, 4551.366]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(mixing_matrices, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[30.3277438, 30.3277438],\n",
       "       [30.3277438, 30.3277438]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(mixing_matrices, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_scored_mixing_matrix = (data_mixing_matrix - np.mean(mixing_matrices, axis=0))/np.std(mixing_matrices, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 483
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "plt.matshow(z_scored_mixing_matrix, fignum=1);\n",
    "plt.xticks(range(z_scored_mixing_matrix.shape[0]), [unique_values[l] for l in bins[0][:len(unique_values)]]);\n",
    "plt.yticks(range(z_scored_mixing_matrix.shape[1]), [unique_values[l] for l in bins[0][:len(unique_values)]]);\n",
    "for (i, j), z in np.ndenumerate(z_scored_mixing_matrix):\n",
    "    plt.text(j, i, '%.2f' % z, ha='center', va='center', \n",
    "             color=\"white\" if z < z_scored_mixing_matrix.mean() else \"black\", fontsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "**Exercise 1** \n",
    "\n",
    "Rewire the network with different models and compare against the data. For this, change the parameter ```model``` in ```random_rewire``` (see the <a href=\"https://graph-tool.skewed.de/static/doc/generation.html#graph_tool.generation.random_rewire\">documentation</a>). \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "\n",
    "**Exercise 2** \n",
    "\n",
    "Load your own data set (or use a data set from the library) and study the mixing patterns in the network. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}