Basic NetworkX Graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic NetworkX Graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import required libraries\n",
    "\n",
    "This is a light notebook to walk through basic NetworkX functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set up the Graph\n",
    "\n",
    "Options in include Graph (undirected), DiGraph (directed graph), MultiDiGraph (directed graph with multiple edges permitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add nodes\n",
    "The formats for adding nodes is G.add_node(<node_id>, attr_dict={kwargs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G.add_node(1)\n",
    "G.add_node(\"Sam\")\n",
    "G.add_node(1.567)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 'Sam', 1.567]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add edges\n",
    "The format for adding edges is G.add_edge(<source_node_id>, <target_node_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G.add_edge(\"Sam\", 1.567)\n",
    "G.add_edge(1, \"Sam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 'Sam'), ('Sam', 1.567)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding attributes to nodes and edges\n",
    "\n",
    "By adding attributes to nodes and edges, you can make your graphs more informative and perform multiple functions to better understand your subject matter.\n",
    "\n",
    "Add attributes to nodes and edges the same as you would to a regular dictionary using \n",
    "* G.node[node_id] for nodes \n",
    "* G[source][target] for edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G.node[1]['Number'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Number': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.node[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "G[1]['Sam']['weight'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Sam': {'weight': 5}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weight': 5}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G[1]['Sam']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Working with your graph\n",
    "From here, we have a fully realized graph and can perform multiple operations on it.  Graphs at this stage are also suitable for export to JSON or other formats for rendering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 1, 'Sam': 2, 1.567: 1}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See number of connections per node\n",
    "nx.degree(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.degree(\"Sam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'Sam', 1.567]\n"
     ]
    }
   ],
   "source": [
    "# Find connectors between nodes and paths between\n",
    "for path in nx.all_simple_paths(G, source=1, target=1.567):\n",
    "    print(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's complicate things a bit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "new_nodes = []\n",
    "\n",
    "# Add 10 new nodes\n",
    "for i in range(1, 10):\n",
    "    G.add_node(i)\n",
    "    new_nodes.append(i)\n",
    "\n",
    "# Make sure each node is connected to at least one other node\n",
    "for i in range(1, 10):\n",
    "    G.add_edge(i, new_nodes.pop())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 9]]\n"
     ]
    }
   ],
   "source": [
    "paths = list(nx.shortest_simple_paths(G, 1, 9))\n",
    "print(paths)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drawing our graph\n",
    "From this point you can export the data to a different format or render it via Matplotlib.  NetworkX documentation suggests that you use a dedicated tool to visualize your graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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lj6XcP1di3uPGcOrP/ozlnZ0cGR7O+Ny55pzzNQ28NzVFW1ubg6NIkuZ8q0w8\nHmd83Np6u729XXMzFSIej/OJhQsxxjAB83rezbX5Q8AsWMA7771XU+/TfXv38sgDD/DMuXNcm+H7\nR7CG5VcB3cB/wRqqz/TYVMeA2wMBdjz0EFu3b5/1/Vxzzvk4Sm3N0Zeaer5VIJFIEIlE6AuHWdrZ\nSX9PD/09PSzt7KQvHCYSiWhJR5m1tray6ooraKP4wAuVMe/m1vzhpcDKyy+vqcALsHX7dh4+dIhb\nW1pYEwoxAvw65ftrgceA/wo8i5WYN1fgxX7Myx99xCMPPpixB5xrzjkf+5ub2bxzZ9E/L2nKuspY\nHJurrN9TYG4IBEx7KGQODQ6W+3Rr2qOPPmoWu1BModybo7tVOnMRmIEKLBzilfTSrkv9ftNpFya5\nEUwbxe9ylKkghopsVBYF33ksW43W9KpJy8EsAdMA5vJPfKLmC9yXy7vvvmv8dqOo2IBVCZujF1M6\nM9PraADzq1/9qmyvo5K8++67piMUMs9hleR0usvRLaFQxs04VF6ycij4zlPZPkTJvXlvAbPNbkGn\nlvsLgPlEXZ1paWoy3z18uNwvo+asWr7c+e41FbDJght7Fa/u6ir3y6gY6TseubLLUZb3iTZWqAwK\nvvNQtuGjATDLwHzDDsBrIOsOMzfbgfiuO+8s98upKUNDQ6bP7y++R5Pn5uhevI5s2+Plc+v1+yvi\ndVSK1MaMa7sc5RghybadaPJnn7bfa7W8C1WpKfjOQ5kufMN24P26/W++rdoOMF9SAPZMtcy7Vcvr\nqATpOx65tdHCXLkB2k60vBR856H0Ib9JrJ7uH9uBt9D5nA7QELSHqmXerVpeR7mlJ7B5FXxTaTtR\n7yn4zjOZ9gUdAvNZOwAX2xNpa2pSK9dD1TLvVi2vo5zSg29y2Hm+J+ZJbgq+80ymZR69YH4fZ9mR\nv1lfrzk4j1XLvFu1vI5yyZQ9XsqEK6kMCr7zTKZWcgArq9nph/WG1avL/fJqTrXMu1XL6yiX9Kkk\nx0uNKiQxT7JTecl5Jlna74PpaXzAz7CKrU9i1Xx1VO5vwQJ+UWOl/ipJPB6/ULmqra1t3v4dquV1\neCkSiTC4cSMv2PvtJrDqOT9P4ZtXHANubWnh9NgYDQ0N7p6ouEblJeeZ1tZWrr7yygsbb/+l/W8n\nzssWttfQDjOVqLW1lRUrVrBixYp5HbCq5XV4KX3Ho7l2OcrmNNZWjZdeeilHjx5VWdkKpuA7D6XW\naD0CfOghewdVAAAfbElEQVTScevq6lw6kogUItuOR2fhwl7QczmGtSPS/wp84/RpBjduzLnTkZSX\nhp3noUQiQdeiRRw5c4ZbsXY/eR1r2NlX5DHLsVuOdmASmSm549G6c+c4grWb0c+5uMvRZuA2Lo5y\nTQPPYW3PeBKrt7wu5Xhz7XQk5aOe7zyUbCX/W7+fDqwPZitcGIouxnPANStXljwAagcmkey2bt/O\n7V/5Cn/Gxd2M1mENJ38F+DoQwpoP7sbalnEAuMd+zLq0482105GUj3q+89j/9rWv8aff+Ab/ACzC\nSsx4schjfTYQYNPgIOvXr3fvBNMcGR7mvk2bWG0Mm8+e5XPMbMF/H9gfCvFGfT0DBw6wroTnIlKJ\nkqNaz585MyvRKpmEdQRYbt/XhtXwnouSsCqPer7z2P/8B3/Ah5dcQj3wbeD/hgsJG4U4Bpy65BLW\nri12m+257du7l/s3bOAHZ87ww7NnuZ2ZCWI+rH1MX5iY4AdnznD/3Xezb+/ekp2PSCUaGRlh1fnz\nGTOcR7CGnm8CVti3fMeprgVWnj/PyMiIOycqjin4zmOtra1cs2oV3wfuBP4H4F9ReHbkb9fVse/g\nwZK1iI8MD/PIAw/w8rlzrmwKLlKt9u/ezWZ7udGs72HN+RZr88QE+3fvdnAEcZOGnee59PWBXwb+\nE/AXMGegOwb8NvDbd97Jdw4fLsn55RpGm4uGyqSWJNfwx6anZy0bjANLcWEtv8dJlZKder7zXPr6\nwO8AjwH/ErgBa6jq1ymPnwaOAv1Y6wETjY0cGBws2fmlD6PFgah9i8/xsxoqk2oVj8eJRqNEo1Hi\nceuTMD4+TqffnzG4juPOWv4OreWvGAq+81ym9YF3AL8EtgJ7gEuZnR15O9DY1MSB73zH9V5l6oVl\n3ze+wT0TE0SwKnEtxQr8/fb/+4AIkC2/WUNlUi3myvR/7rnnrOKQUhvKV9lS3JRrd5kYmKh9i5Vo\nd5nJyUkzNDRkent6TNDnM92hkOkKBk0DmIVgVoE5kqHo/lG7hu1iMIewtlMbtc9Tu7NItUhuPrGm\nudmMpHwOYmDeBPNtMDcGAsZP5t2MtNNR9VHwrSLl2l0m24UlU4AdznJheBVrX+FOrL1Mg1g7uzwB\nZkljo/nxj3+si4bMS+kN40l744Re+33enfKeX4S1Q1kiw2dEOx1VFwXfKuP17jIF7+cKZiDL99+2\nv38YzFNgVoPx2wF5aUODCfp8prenxwwNDWmHHJkXhiMRs6ypybxtv8eH7UboGsjaUL0+S0NVOx1V\nF2U7V7FS7y5zZHiY+zds4OVz5y4s+p/Laaz6sw8zuxoPwB8Du4Drgd8DFeKQeSs9038f8AhWych8\nViLcDuzAyt0A7XRUdcod/WV+mpycNItbWsyxIlrgr9ot+/ShtQG755t3L9rleWsRNw0NDZl+e+/t\nYfu9/XYBn5PkSFBqD7jo4wQCrk41iXPKdpai5KrEM5drgZVYy6CSjmD1CpL1bPM5hgpxSCVLFsxI\nYNVf/x7kPUKE/dhn7J9NrgZYh9Ub7qWAnY7sjRU0SlRZNOwsRekLh9l24gTFFqQ8irXk6SU0nCbV\nJ7Vgxn8EBoEXijzW9fbtW1ycgvlzrOHoK7CCcfpORxHgT4NB/q6ujocHBvjyhg1FPruUinq+UrB4\nPM7xU6e4zcExbsOqQx3nYs3aonvRKsQhFSa1YIbTspA7gEhjI5f6fHQHg3QHg2zy+fgXV13Ff7dl\nC49edRWX+nx0BQJ0+v10AhuBf/j1r2k2hi1f/eqsHcMyFfkQb6nnKwWLRqP09/TwVpYatPnqxtqF\n6YvANnDWiw6Heen4cUfnI+KW5GfkpxMTrpWFPPXzn/Pxxx8DsxMov3PoEDvvu4/VxnDvhx9mTVQ8\n/vHHdHZ28o+//CWdfj8AY4kEV195JZt37uR3fud3NILkEfV8pazOAMfBeS/65Em14KVitLe3M5ZI\n8E+4Vxby448/ZsWKFaxYsWJG4N23dy//+7338p8nJvjRhx/m3DHsv5w7x9nTp3loepq3JiZ4a2KC\nD6an2XbiBIMbN7K8s1M5FB5x8p6QeS4ejzM+Pg5YF4vW1taM96VLXlimsT7YxZgG3rP/72bNWhWM\nl0rQ2trK1VdeyY9OnCjp86TuGJZPMte1wE+wEraWYCVwJYPz2okJa4nT3Xfz7jvvsHX79pKdt6jn\nW3My1Ze95aqrWNzeziebmvhkezv9V101o+Zs6lwRXLywfN/BeTyHNcfb7PQFiVSozTt38h+CQcaw\nGpvFmgbem5qira1txv2JRIL7Nm3iewWss4fMWdRJWkXgofKudBIvZSoDmU/Fnf5QaFZJytQ1jEVV\n2wETQTVrvRaLxczo6KgZHR3V76vEkmvhr6U0ZSHd+gxmXYvf0qJKciWk4FsjMpWBdFLUws0iG6pZ\nWxrJQHvq1CnzxBNPzNj0ojsUUrlODwxHIqbN5zOfdfD+zlYWsrenx/nnJtfzhkIqR1lCCr41IL2+\nrMGdSjmZjpvXMZhZtWcIzM0luDjVovTdpTr9fhMA82kKG9kQ9+zZvdsEoPiGaoYeaCwWM0Gfb8bf\ns9DbFNaoUyzL99WoLS0F3yqXqYc6idXzdONi4GRjheROSzeD6xenWpQ+rbAXleusFFu3bDEduFcW\ncnR01HQ7GHJO3rqwthrNGpw1nVMySriqcpnKQLpZ1GLr9u08fOgQt7a0sCYUYgT4dcrjp7HW4X4G\nuBn4FbAXWIqVbPXHwCbgIPB5rI0X8nUauD0QYODAgZpfm7hv717u37CBH5w5ww/PnmUKeBSV66wU\n/6y7G7NgATeQf1nIa4DG1lbOGzMj4dErqasIpATKHf2ltDLNC5VijjXbVoaBSy4xC+vrzUq/3wyD\nec9uaUfBHATThrWF2lH11IqWPvzv5siGOJf690kmOPbb7/mMe27bj/ku2acFksPOjhMVyT7sbMB0\nBYMmGo2W8bdXvRR8q1imeaFkdrHjuaIcw1GxWMxEo1ETjUZNLBbLucfwjVddZbZs2WJ6r7rKBH0+\n09HQYAJgrst1cWpu1hylLdO0guN9X5Vo45pMf58EVpbxp7D2q16GNfwbxEqAijB7x69Mjc1SJ1xp\n2Lm0FHyrWKZ5oVEw3Q4+sE5bxOmBOdP33nzzTTM4OJgxWPeFwyYSiahnZsu03ETZ45Uj23KgZMLj\n61wcCcrVAzXMngMu5VIjvQ9KT7Wdq1imGsxRoB94y+Gxu4NBXnz9dVasWOHwSLnF4/ELc07p9Wxl\n9u5ScXCtlvAvxsb0+3Yo0+5fbu3iZYyha9Einj9zprjjYOVNZMuW6G9u5p6DB1mvrQhLQglXVSy1\nDOSF+6BkFXdKobW1NWM9W8m8u9Q47pbrlOJl2/3LrYRHv9/PwIEDfL6pqfBERawtPbMF3mPAybo6\n1q4tdrsTmYuCbxXLVAayFbganJeGXLlSwbDMUretk8qT7e/jdIvBzRMT7N+9G4B169ezY9cuepua\n8s6i7sXapnBdlsdoFYE3FHyr3OadO9kfCs28D+sCUKz9zc1s3rnT0XlJacy3kY1aE8f9XbzyXe53\nM9ZQ88PA1izHPgb0BgLseOgh1mm4uaQUfKvc2rVreaO+ntdS7wPegBn3pYpjzQ1H7f+n0nBU5cg0\nraCRjfJLblR/9uzZWX+fUk0LrFu/ntNjY3zl8cd5LBzmUp+P7mCQ7mCQhT4fX+/q4qdNTVwZDOIj\nc3Dub27m1pYWHh4c1I5GXihzwpd4IJ/ykpNYS1R67SUP3fYtaN83BOb/I3vFHSmPTMtNHC81UrnO\ngqWX9UzWz25Nyzz3arVBplUFuZb8aRWB9xR8a0SujRW+wdw7G92MVQLyrjvvLPdLkRSZlpuoyIa3\nMu0Wlvx9PgXmJqxlRKNgfmp/jsq9i1euJX/iDQXfGjIciZi2pibzaS4WsPgSmA5UVWq+yra7lBsb\nZ8jcctU2nwTzJJiFdsBNjib5wazGGqFIL6aRz03rb6uD5nxryLvvvEPAGG4DHgNCwH/CmsdV/d/5\nKdtyk3VYGa295F9LWIk2hTkyPMwjDzzAy+fOzfr8HMFay3sYGMTKnXjLvp0Fvm7fv9x+bCGU8Fgl\nyh39xRuZ6v8u0tBk1cjWA8urlrDKdRYs137WBe+TzcWdvvTZqx0KvjVA9X9rQ3LusT8UmhFoE2AO\n20OdfqxphqV+vxJtHJirbKTTPa41LVD9VF6yBkQiEQY3buSFlDKTfcA2oNgFQ0eBgXCYl44fd+EM\nxS1TU1OMjIywf/duXjt5kg67SMJ7U1Ncs3IlX9qyhRtvvJGGhgaV63SgJGUjyV7u8RhW4YsdDz2k\nZUBVQsG3Bqj+b21SXezSiMfjLO3sJDY9PePzE8Gax32hyOPeBGwE7rC/nsZac72/uZmTdXUMHDig\n+fgqosp0Vc6L+r+6qFem1tZW/W1K4ELZyOmZdcT2Y40mFWsrcDfwtWAQuDhasXnnTtauXatSj1VG\nwbfKZbtQiIgzcayGLFgXUjfKRv7a5+PZv/kbWlpaNFpR5RR8a1Bq/V9fkcdQ/V+pRYlEghdffJEP\nJiZYijWCBPAu0II7o0ktLS0l36pTyk/rfKuc6v+KuOPI8DBdixYxvG0b38HKmUiu3T1O8Q1ZqU0K\nvlUu07aCoJ2NRAqxb+9e7t+wgR+cOcMPz57ldmb2cj8BfIB2k5L8KfjWgEzbCs61s1Eux+yf1c5G\nUgtyVbJK0miSFErBtwZk2lbQDwwAn4cZZQnnchr4LSD+4Yf0X3cdkUiEqakpF89WpHIkEgnu27SJ\n7507x/I5HqvRJCmEgm8NcLX+L/AgcPb8ebadOMHgxo0s7+xUrWepSiMjI6w6fz6vohlOR5O0T3Zt\nUfCtEevWr2fHrl30NjXNCLRbgYexquusAUbIvNH2dcDNWD3lr2Ill6wFXpiY4AdnznD/3Xezb+/e\n0r8QEQ/t372bzSmV4XJxMpp0eyDAwIEDWstbQ1ThqsYcGR7mvk2bWHX+PJsnJrgNK3FkCtgO/Dlw\nDuiw738Pq1TeRqyA+zhW634Aq+ecdBprV5yHBwdVhUeqQrZKVnPZBzwCPMPcu4WpbGTtUvCtQZnq\n/340NYWZnuYvgP8WeN9+bBtWMkmqY8DtWEPWW9Puv7WlhdNjY2rBy7wXjUbp7+nhrTx7vqmOAPcB\nq7DmgpONXFDZSLEo+Na4eDzOU08+ybd27uRvJifnTCpJOo01//swM3vA/aEQ9zz+OOt1MZF5Jh6P\nMz5u1axqb29nfHy86OAL1mjSCHAvMIk1mgRWgZuWxkYe/c53VDayhin41rhEIkHXokU8f+aMKzux\naLcjmU8SicSFUaDjp07R6fcDMJZIsPqKKzjxs58R//hjR5XgFgKngI/t+5qBbm1KUvOUcFXjCsnm\nTHctsBKrdZ90G/DayZPE43FXzq8Y8XicaDRKNBot63lIZUtWrDq0aRPbT5wgNj3NWxMTvDUxwQfT\n09z/xhtcaozztbvAcmCFfevg4qYkUrsUfGtcIdmcmaSvbUzd7chLiUSCSCRCXzjM0s5O+nt66O/p\nYWlnJ33hsNYjywxzVaxKZvPvOX+eP3HwPPuxPiMi6TTsXMOKzeZMlRxW+wUXE7O6g0FefP11z4rD\nJzO4VxvD5rNn+Rwzk1u+D+wPhXijvl7JLcKR4WHu37CBl/MonJEAuoDnwZVpGdBe2GLRrkY1zI3t\nBn1Yw2jvYwXfTPVp0xNZ3Lzg7Nu7l0ceeIAfZCn9l+zBrJ2YsLK0776bd995R8s6alSyYtXzeQRe\nmLl292UoKCHxdvtn09OpVEZSQMPO4rLkhaWxsbHkw8D51NxNdS3w8kcf8ciDD6oiV40qJseh2Epw\nO5i5EiBJZSQFNOxc05LDzh9MTzvO5kwOO/c3N7PqS1/iyJNP5hwG/vfBIG/U1bF7YIAvb9hQ8PM6\nztLWeuSa1BcOs+3ECYop4ngEa/72CqzAmnHtLnCS2UVokvTekyT1fGtYtu0GC5HM5mzFurC8NjXF\nM088MWciy//54Yf854kJ/uDuu/nnS5YU3BN2nKV9/jwjIyNzPlaqRzwe5/ipU9xW5M+vwxpOfg3Y\nA1yKNQzdidUAHQDusR+TKfCqjKSkUvCtcZm2GyxEMpvzNPCvfD4CxhQ0DHwM+PCXv+QbX/5yQRs0\nOM7Snphg/+7dRf98IbT0qTJcyHFwcIwg1t6938Ua7fkr4L/BKin5ErCe2XO8YA9F22UklfAnABip\naZOTk2ZxS4s5BsYUeHsVzGIwPwFzWVOTWdjY6Pg4ywIBM7BnT85zjsViJnDJJWa6iOdK3qbABH0+\nE4vFSvZ7HRoaMr09PSbo85nuUMh0h0Im6POZ3p4eMzQ0ZBKJREmeuxLFYjEzOjpqRkdHS/Y7n8vo\n6KjpDoWKfs8kb11goilfPw1mSX29OQoz3pNT9vduaW42i1tazHAkUpbXLZVJwVfMcCRiljU1mbcL\nuAC9DWYJmJV+v1nc0mK23Huv6XdwYbsFTMQ+7rJAIOeF6tSpU6bT4QXUgOkKBk00Gi3J73NxS4tZ\n09xsRjJckI+CuSkYNO2hkBl47LGyBaNSq7QGSCwWM0Gfz0w5bbSBiaXd13TJJeb61atN0OczXcGg\n6QoGTdDnM33hsIlEIjXV0JL8KPiKMcaYgT17zLKmJvNqHhegV8F0gLl8yZILF5benh5z1MFF7Wkw\nfSnHX9zSkvWC9eijj5rFFRp8c/0eJ8EMgem1L+DLwXSCaaqvr7recD4NkP5QyPMe4Y2rV7v2Ps30\nXorFYiYajV74v0g2Cr5yQfKC2R8KZR1CuzkYNJ2hkDk0OHjh55I9CsfDwCk9iltCIRPJclH+F5dd\nZgL2zzh6PpeHnYcjEbO0sdH8NZhRZvaOhrGG1tdARQWjUii0IZfPVIMbhiMR09LUZK538L5JjtB4\n0ZCT6qbgKzMkEgkTiURMXzic9xBaKebSngbTFw7POr9v7tpl/GButAOWkx7MDatXu/I7m5ycNE8+\n+aRZWF9vAmC67VsQq5d7F5hldqCppGBUCsVOYcw11eBUskHwit0IcpKbkEi7v9T5A1KdFHwlq3yH\n0EoRfDNd0IYjEfNJv990YQ3f9jt4ruvADAwMOP4dJUcLbmpszNij3Q5mkR1gKikYlYLj5L0cUw1O\npDcIhu3GUPrfJIY1YpE+anHhb2L/bKaGXKaGokguCr7iWKkSWVKH8pIX9mexepXvgmkH82yGC2U+\nF/oAmDfffNPR655reHUSh72sEgWjUhkaGnKWdJdhqsFplnS2BsGAHUxfYeY8fPqoxRB2Fr79MxnP\nu7k56xSJSDYKvuIKNxOuMgXfoaEh89lg0DwBptW+OF6GlbCUeqFMHxLM2KsE46+vdzRMmNqbytZj\ncto7zzXvXYlceQ+Ew65mSedqEGzFaoTdQPZ5+Ovtx2zNcs7zsZEklUHBV1zhuNfDzESW9GHnK5Yv\nN+3MkbCE1dPMNDSYvFAuA3M3zoYJJycnzaLmZvNH5O4xuTEvPV+GM91Kumusrzedzc2uZUlnaxAk\ne755z8Mzu+c7X6cHpDIo+Ior3CjWkdprTQ0839y1y3QUeaG8UOggJTA7HSbccu+9JjRHQ+AmMP60\n7xUTjOZLIo9b8/4dWFMJef2d50hMy9YgyDbnm+uWPuc73xPjpPwUfMU1RWe6Mru3mgyQw5GIuayx\nseBjdti3INZwdgQruDsdJhzYs8csqqubsyEwirWO12kwmi9LWNwKvsuZWT1qzvdOjp5npnNyOg/f\nBubmKlkSJuWl4CuuKniNJ7OH85IB8syZM456051gfpV2sb6ssbHoi2YhjYtRrGHoWgm+pUq6S7+l\nz6/nakxlCr5O5+F/s77ebNmyRXO84piCr7gudflN1nq3ZJ6fTe3NuDmPnKzK9c1du4p6TYUOq8fs\nQFJphUBK6fpVq0pSPSq9Mlj6/PqVfr85fPjwrPPJ1CDopXbm4aWyKfhKSSSLdXxqyRLjx+rhdjF7\nGDj1wpY+j+ZG9uxqOwi3gfnU8uVFv55iGgKVcKFPX6pTyg0OHn30UfNpB683U/WofCqD3QQmVF+f\ncUQj9T2UbBDVyjy8VDYFXym5Q4ODpiMUMjcEAuapDBfQTDu/uJU96wcziDVP5yTJqpiGgOOlRkUm\nhqUv1ekKBk2H329a7d/HZX6/6barlrlZU/rUqVMmgHvVowrOSG5qmpUAldpoqrWpAKlsCr7iiULL\nVrpZNetZnCVZFdsQKEeRjfQNDf4c72pKx2Ix46+vd5xJbHCQkZyWgJU6XaDgK5VEwVc8l0/ZSjez\nZz/pIMnK6bkUG0Q6wGzdsqWg80xPdiu45+jC0pnenh5zd4HP28HMpDu3Gy3JRLnXqb15eKlcCr5S\nkdzKnvVTfJJVktOGQDEFHXYUGAzzrV88V9DPtXQnn/nj5DBvcq62H3Im3S2y/0apf+dSVAZLNkyu\npfzz8CLGKPhKBXMl4aqry/F5uNEQ+C6YJjCfnSMYpWaA51tBKT0T282eY6Hzx08++aRZ1NxsjmHN\n30awEuyCWFMAXcxOuktPTCtVoporWwqqjrO4RMFXKlYpCvUXy42GwNUZgtFy+/83kD0DfK653/Tf\nk1s9x2Lnj9uamky7zzej1x3DKp4RZfY63iGsRknycaXMSD5z5oxpa2qqmc0upHIp+ErFqqQt6pw2\nBH7TDq7JNau/idUTXpoSgLNtDjFXIyK9YeBGz/FTS5Y4mj/+pM9n2hcsyOvxr8CFLGkvkqIqdc9h\nqS0KvlLRKuVC6bQh0I419Dxnz5HZxUdyzTOmZ2K71XP0g3nd/rrY+eMlDQ2mtbHR9IdC2Yfa7SVm\nW7dsMcuamsxfexB8jSmiEpvqOIvLFHyl4lXKhbLYhkAHmN+l+F10cg2jpieDudVzXIY1ROzG/PHh\nw4fzWmI2sGePWdrYaAJ4k5GcHFbPp3GgHq+4TcFX5oVKuVAW2hC4rLHRtF5yieO1r9l6cqUKvl12\n8HUz8zifJWbDkYhZWF/vWUZyoevPRdyi4CvzRqVcKAtpCOx55BFXqj5lC77pmdiu1ZS2j1WOEplP\nPfWU+UxjY/EBv8iM5HwaByJuqTPGGETmmXg8zvvvvw9AW1sbra2tnj7/1NQUIyMj7N+9m9dOnqSj\noQGA96amuGblSjbv3MnatWs5fPgwB7/yFf62yOfpB74MfNXn4xdjYxlfZ184zLYTJ1ib/BrYBhe+\nLtRRYAD4PrAUiAELijzWNLAwx7lnkkgk6Fq0iOfPnOGaAp/vGHBrSwunx8ZosP8mIpVIwVfEoVwN\ngfTAWKijwL8DLg2Heen48YyPiUQiDG7cyAsTE9bXwCDwQpHP2Q/cA1xn//+tIo+T1B0M8uLrr7Ni\nxYq8f+bI8DD3b9jAy+fOsTzPnzkN9AYCPDw4yLr164s6VxGv1Jf7BETmu9bWVlasWMGKFStmBN54\nPM7xU6e4zcGxbwP+DvjSli1ZH7N27VreqK/nteTXwBtw4etCHANOUnyv2S3r1q9nx65d9DY1cSyP\nxx/DCrw7HnpIgVfmBQVfkRIZHx+n0+8vesgWwAc0AzfeeGPWx/j9fgYOHODzTU2cBvxYw8afx+oN\n5us08FvAHqABaAfGsIaOizWNNRTf1tZW8M9u3b6dhw8d4taWFtaEQowAv0479lGgv7mZW1taeHhw\nkK3btzs4WxHvKPiKVDi/3z/n/GV6T3EdsAPohYJ6jgs/+Uma7Ptagaux5n6L9RxwzcqVRc/Jr1u/\nntNjY3zl8cd5LBzmUp+P7mCQ7mCQhT4fA+Ew9xw8yOmxMfV4ZV7RnK9IicTjcZZ2dvLB9DS+Io9R\naMLSkeFh7tu0iVXnz7N5YoJJYDuwCtiMNYyd7IlPYwXH/c3NnKyrY+DAAc4b4+78cXMz9xw8yHqX\nAmO5E+1E3KLgK1JCbiRcDeRItsokPRO73efjo48/ZjqRYBLo8PtZsGDBrMzshoaGWZnGCaALeB6U\neSziIgVfkRJKz0QulNOeY3pPEZiz55ieaXwEuB94GZR5LOISBV+REpqva1b37d3LIw88wDPnznEt\nsA94BHgGuHaOnz0G3G5nHisBSiQzJVyJlFB6JnK+TmMFsIEDB8oyZJueaXwZ8E3gVmANKPNYxCH1\nfEU8kN6TzKWSeo5O5o9FJDsFXxGPpGciz5V5XGlzpcXMH4tIZgq+Ih7Ktya0eo4i1U3BV6RMtGZV\npHYp+IqIiHhM2c4iIiIeU/AVERHxmIKviIiIxxR8RUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdE\nRMRjCr4iIiIeU/AVERHxmIKviIiIxxR8RUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4i\nIiIeU/AVERHxmIKviIiIxxR8RUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4iIiIeU/AV\nERHxmIKviIiIxxR8RUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4iIiIeU/AVERHxmIKv\niIiIxxR8RUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4iIiIeU/AVERHxmIKviIiIxxR8\nRUREPKbgKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4iIiIeU/AVERHxmIKviIiIxxR8RUREPKbg\nKyIi4jEFXxEREY8p+IqIiHhMwVdERMRjCr4iIiIeU/AVERHxmIKviIiIxxR8RUREPKbgKyIi4jEF\nXxEREY8p+IqIiHhMwVdERMRj/z+NNhLH9P/R+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2db6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The simplest rendering\n",
    "nx.draw(G)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}