{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "web = nx.read_edgelist(\"web-Google.txt.gz\") \n",
    "#you can download this file from https://snap.stanford.edu/data/index.html\n",
    "webdeg = nx.degree(web).values()\n",
    "# the list of degress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.erdos_renyi_graph(600,0.2) #random graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = nx.degree(g).values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   4.,   16.,   45.,  104.,  121.,  140.,   93.,   52.,   17.,    8.]),\n",
       " array([  92. ,   97.4,  102.8,  108.2,  113.6,  119. ,  124.4,  129.8,\n",
       "         135.2,  140.6,  146. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gUFV9dOiY9fcJQZK0AYxz6eWkw/544N+B1wPfAe4B3lZVD06sE0nSqk10GaeqfpLkPcA/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109330390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sEAeJmZkVMqMgkXStpKOSnkuvmyX1SzohqU/SktzcbZKGJB2XtDFXXyvpWNq2O1e/TtLT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\niIPEzMwKcZCYmVkhDhIzMyvk/wNY1dJ3l0g2ywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109314d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(webdeg,bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1000, 0, 3000]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1090dffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(webdeg,bins=50)\n",
    "plt.axis([0,1000,0,3000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O0jFJL6TvmyT1STopqVfSotzczZIGJZ2QtC5XXyXpeNq2I1e/XtIzqf6ypFtz2zrSe5yU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\n5P8DiKy7Oz+lpxYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e71350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = plt.hist(webdeg,bins=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = Counter(webdeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c182b0d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vb+A/GI7jS0Cht/+u+/e/rUnfYSwWI1MV/JO+OStI72xyzl0VPL4BiDjnZgaP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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e715d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(c.keys(),c.values(), 'o');\n",
    "#plt.axis([0,1000,0,3000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pg = nx.barabasi_albert_graph(800,3) #preferential attachment graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 747.,   33.,    7.,    4.,    2.,    5.,    1.,    0.,    0.,    1.]),\n",
       " array([  3. ,  12.2,  21.4,  30.6,  39.8,  49. ,  58.2,  67.4,  76.6,\n",
       "         85.8,  95. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FzgDbxtNVSdJSLXXWvbaq5rryHLC2K98GzA61mwXWL/EakqQxueYllqoqoBZqcq3XkCRd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e4cad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pgdegrees = nx.degree(pg).values()\n",
    "plt.hist(pgdegrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13a62ea90>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dk7Rb0gfufnvT9klJT0kaknTU3Z+UtFXSf63s0l+PngESiOO47x7hWa/Pa3Z2TktLwxoZ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\nIfQBICD/D0WzrLc54xf0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cda8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = Counter(pgdegrees)\n",
    "plt.loglog(c.keys(),c.values(), 'o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}