{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Both the outlink and inlink distributions have the vast majority of their mass near zero and decay very rapidly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import statsmodels.api as sm\n",
      "from scipy.io import loadmat\n",
      "from pandas import Series\n",
      "from numpy import array"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "A = loadmat('A.mat')['A'].astype('int32')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "outlinks = Series(array(A.sum(1).flatten())[0])\n",
      "inlinks = Series(array(A.sum(0).flatten())[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Outlink distribution"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "outlinks.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "count    185314.000000\n",
        "mean         13.126801\n",
        "std          46.984216\n",
        "min           0.000000\n",
        "25%           1.000000\n",
        "50%           1.000000\n",
        "75%           3.000000\n",
        "max        2799.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "outlinks.hist(bins=20);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yAMjPz6e9vZ1z584xMDBAe3t78s4wEREZX657a/D27dvp7OzkzJkzrFq1iuXLl7N06VKq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\nRETEN5WJiIj4pjIRERHfVCYiIuLbfwPSOUpS5EiwBwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117202350>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log(outlinks+1).hist(bins=20, log=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CubTzz+O/i22xby8ko3XwhmEoHo+ntuPxuAzDyOSQAJBXXprBZ1TwVVVVGhwcVCwWUyKR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\nq8UlJaIz+NcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x117179810>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "outlinks.hist(normed=True, cumulative=True, bins=30, histtype='step')\n",
      "ylim(.94,1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "(0.94, 1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5eVJTU0lOTubEiRNYlsXevXsj/+NdiM6ePRv584EDB8jJyQHcmc+yLJ5//nlW\nrFjBl770pcjXTbiGs2Uz5fqNjY0xMTEBXLjj6MiRI+Tl5Tly7RbEbyofOnQo6rbTu+++e763dFkC\ngQD19fXAhdnf+vXrufvuu2PeJvbHP/6RV155Ba/Xy/33389nPvOZ+YzwIc8++yxHjx5lbGyM1NRU\nvva1r3Hrrbdedp4Pbns7f/48+fn5lJeXz2esiP/Md++99/L666/z9ttv4/F4uO6663jwwQcjM1u3\n5Tt27Bjf//73ycnJiYwXNm7cyPXXX+/6a3ipbPfddx/d3d1GXL+TJ0/S1NREOBzGsiw2bNjAV77y\nlY/18+Ry8y2IQhARkfk37yMjERFZGFQIIiICqBBEROR9KgQREQFUCCIi8j4VgoiIACoEERF5nwpB\nREQA+H9bXpoUR5BbhQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118a25cd0>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ecdf = sm.distributions.ECDF(outlinks)\n",
      "\n",
      "x = linspace(min(outlinks), max(outlinks))\n",
      "y = ecdf(x)\n",
      "step(log(x+1), y)\n",
      "ylim(.9,1);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x118d4e2d0>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Inlink distribution\n",
      "\n",
      "Same story, but even more extreme."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inlinks.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "count    185314.000000\n",
        "mean         13.126801\n",
        "std         138.280052\n",
        "min           0.000000\n",
        "25%           0.000000\n",
        "50%           0.000000\n",
        "75%           2.000000\n",
        "max       46769.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inlinks.hist(bins=20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1187fa890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1188e2f90>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log(inlinks+1).hist(bins=20, log=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x116f0f110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x116ef3050>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ecdf = sm.distributions.ECDF(inlinks)\n",
      "x = linspace(min(inlinks), max(inlinks))\n",
      "y = ecdf(x)\n",
      "step(log(x+1), y)\n",
      "ylim(.95, 1);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x118c732d0>"
       ]
      }
     ],
     "prompt_number": 65
    }
   ],
   "metadata": {}
  }
 ]
}