3-Network-Analysis-with-NetworkX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ISRC Python Workshop: Network analysis\n",
    "\n",
    "__Network Analysis with NetworkX__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@author: Zhiya Zuo\n",
    "\n",
    "@email: zhiya-zuo@uiowa.edu"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:24.378517Z",
     "start_time": "2019-03-27T21:22:23.592154Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Network analysis, though well developed through the area of physics, has been deep in various research fields inclduing economics, finance, computer science, biology, etc. In this workshop, we will cover basics to <a href='https://networkx.github.io/documentation/stable/', target='_blank'>___NetworkX___</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before that, I here introduce two other popular Python packages: <a href='http://igraph.org/python/doc/tutorial/tutorial.html', target='_blank'>___iGraph___</a> and <a href='https://graph-tool.skewed.de/static/doc/index.html', target='_blank'>___graph-tool___</a>. Note that the installation of the latter may be painful but has trememdous speedup compared to the other two. (On Mac, you can easily use <a href='https://brew.sh/' target='_blank'>Homebrew</a> to isntall it!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"mark\">\n",
    "Note that in the following, I will use the term __graph__ and __network__ interchangeably.</div><i class=\"fa fa-lightbulb-o \"></i>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Sample graph data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___NetworkX___ provides empirical network data for you to play with. We will be using <a href='https://en.wikipedia.org/wiki/Zachary%27s_karate_club' target='_blank'>Zachary's karate club</a> as an example here. \n",
    "\n",
    "_Note that if you have not installed __NetworkX__ yet, please run `pip install networkx` in your terminal to have it installed first_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.317673Z",
     "start_time": "2019-03-27T21:22:24.380814Z"
    }
   },
   "outputs": [],
   "source": [
    "# first import the library\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.331157Z",
     "start_time": "2019-03-27T21:22:25.320497Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the version of NetworkX\n",
    "nx.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.341040Z",
     "start_time": "2019-03-27T21:22:25.333695Z"
    }
   },
   "outputs": [],
   "source": [
    "G = nx.karate_club_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`nx.info(G)` returns the basic description of the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.354836Z",
     "start_time": "2019-03-27T21:22:25.345091Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Zachary's Karate Club\n",
      "Type: Graph\n",
      "Number of nodes: 34\n",
      "Number of edges: 78\n",
      "Average degree:   4.5882\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can save the graph as a <a href='https://en.wikipedia.org/wiki/Graph_Modelling_Language'>___GML___</a> file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.365429Z",
     "start_time": "2019-03-27T21:22:25.358833Z"
    }
   },
   "outputs": [],
   "source": [
    "nx.write_gml(G, 'sample-data/karate.gml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Comparison of different libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### NetworkX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:25.392826Z",
     "start_time": "2019-03-27T21:22:25.369240Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Zachary's Karate Club\n",
      "Type: Graph\n",
      "Number of nodes: 34\n",
      "Number of edges: 78\n",
      "Average degree:   4.5882\n"
     ]
    }
   ],
   "source": [
    "G_nx = nx.read_gml('sample-data/karate.gml')\n",
    "print(nx.info(G_nx))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:29.684434Z",
     "start_time": "2019-03-27T21:22:25.396376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.23 ms ± 1.14 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit nx.closeness_centrality(G_nx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### iGraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:29.857060Z",
     "start_time": "2019-03-27T21:22:29.687456Z"
    }
   },
   "outputs": [],
   "source": [
    "import igraph as ig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:29.878018Z",
     "start_time": "2019-03-27T21:22:29.860492Z"
    }
   },
   "outputs": [],
   "source": [
    "G_ig = ig.load('sample-data/karate.gml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:29.888744Z",
     "start_time": "2019-03-27T21:22:29.881869Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IGRAPH U--- 34 78 -- \n",
      "+ attr: club (v), id (v), label (v)\n"
     ]
    }
   ],
   "source": [
    "print(G_ig.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:31.131242Z",
     "start_time": "2019-03-27T21:22:29.891794Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144 µs ± 19.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit G_ig.closeness()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### graph-tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:33.263595Z",
     "start_time": "2019-03-27T21:22:31.140875Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhiyzuo/.virtualenvs/py3/lib/python3.7/site-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/Users/zhiyzuo/.virtualenvs/py3/lib/python3.7/site-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/Users/zhiyzuo/.virtualenvs/py3/lib/python3.7/site-packages/graph_tool/draw/cairo_draw.py:39: RuntimeWarning: Error importing cairo. Graph drawing will not work.\n",
      "  warnings.warn(msg, RuntimeWarning)\n",
      "/Users/zhiyzuo/.virtualenvs/py3/lib/python3.7/site-packages/graph_tool/all.py:40: RuntimeWarning: Error importing draw module, proceeding nevertheless: No module named 'cairo'\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "import graph_tool.all as gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:33.308260Z",
     "start_time": "2019-03-27T21:22:33.266724Z"
    }
   },
   "outputs": [],
   "source": [
    "G_gt = gt.load_graph('sample-data/karate.gml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:33.317991Z",
     "start_time": "2019-03-27T21:22:33.312647Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Graph object, undirected, with 34 vertices and 78 edges at 0x12f44b9b0>\n"
     ]
    }
   ],
   "source": [
    "print(G_gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:41.994992Z",
     "start_time": "2019-03-27T21:22:33.320897Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.09 ms ± 230 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit gt.closeness(G_gt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see ___NetworkX___ is not very fast. However, it is noteworthy that the its well-written documents, ease of use, and all other features make it a very popular Python package for network analysis\n",
    "\n",
    "(By this, I am not saying the other two are not as good - they are powerful and defintely excellent libraries for network analytics!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating a network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Get a network from scratch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with a brute-force approach of building a network. This will give an emptive graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.006441Z",
     "start_time": "2019-03-27T21:22:41.998649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 0\n",
      "Number of edges: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "G = nx.Graph()\n",
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then start adding nodes/edges. \n",
    "\n",
    "_In some other areas, nodes are also called vertices; edges are called links._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.040593Z",
     "start_time": "2019-03-27T21:22:42.013424Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "G.add_node(1) # add one node at a time\n",
    "print(G.number_of_nodes())\n",
    "G.add_nodes_from(range(2,5)) # add a list of nodes at once\n",
    "print(G.number_of_nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we have something in our network now, we do not have any connections between them. After all, connectivity is the most interesting thing in networks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.057185Z",
     "start_time": "2019-03-27T21:22:42.047681Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "G.add_edge(1, 2) # add one edge at a time\n",
    "print(G.number_of_edges())\n",
    "G.add_edges_from([(2, 3), (1, 4)]) # add a list of edges at once\n",
    "print(G.number_of_edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.065516Z",
     "start_time": "2019-03-27T21:22:42.060345Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 4\n",
      "Number of edges: 3\n",
      "Average degree:   1.5000\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now inspect what we have in our first network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.074853Z",
     "start_time": "2019-03-27T21:22:42.068217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView((1, 2, 3, 4))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.084682Z",
     "start_time": "2019-03-27T21:22:42.077028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(1, 2), (1, 4), (2, 3)])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Degree is the number of connections a node has."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.094452Z",
     "start_time": "2019-03-27T21:22:42.087319Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DegreeView({1: 2, 2: 2, 3: 1, 4: 1})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.degree()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sec22'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Graph generators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given specific parameters, we can ask __NetworkX__ to produce some ___random graphs___ for us. One of the use of random graphs is to compare the graph models to real data to see what are the differences/similarities between the two.\n",
    "\n",
    "Reference:\n",
    "> The Structure and Function of Complex Networks. M. E. J. Newman. SIAM Review 2003 45:2, 167-256"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we will use __karate club network__ as the real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.103689Z",
     "start_time": "2019-03-27T21:22:42.097216Z"
    }
   },
   "outputs": [],
   "source": [
    "karate = nx.karate_club_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.110808Z",
     "start_time": "2019-03-27T21:22:42.106787Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Zachary's Karate Club\n",
      "Type: Graph\n",
      "Number of nodes: 34\n",
      "Number of edges: 78\n",
      "Average degree:   4.5882\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(karate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Erdos-Renyi random graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a classic model for random graphs: given the number of nodes and probability of each possible edge formation, the model adds edges independently, also giving a tree like structure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.118305Z",
     "start_time": "2019-03-27T21:22:42.114684Z"
    }
   },
   "outputs": [],
   "source": [
    "n = karate.number_of_nodes() # number of nodes\n",
    "p = 2.*karate.number_of_edges()/(n*(n-1)) # probability of edge creation (This is essentially the DENSITY of a network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.127443Z",
     "start_time": "2019-03-27T21:22:42.121834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34, 0.13903743315508021)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n, p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.136598Z",
     "start_time": "2019-03-27T21:22:42.130821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13903743315508021"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.density(karate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.145008Z",
     "start_time": "2019-03-27T21:22:42.139439Z"
    }
   },
   "outputs": [],
   "source": [
    "erdos_rg = nx.erdos_renyi_graph(n, p, seed=np.random.randint(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.152905Z",
     "start_time": "2019-03-27T21:22:42.148029Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 34\n",
      "Number of edges: 97\n",
      "Average degree:   5.7059\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(erdos_rg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Configuration model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model helps to preserve a given degree distribution (i.e., the distribution of the number of connections each node has)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.164897Z",
     "start_time": "2019-03-27T21:22:42.156724Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16,  9, 10,  6,  3,  4,  4,  4,  5,  2,  3,  1,  2,  5,  2,  2,  2,\n",
       "        2,  2,  3,  2,  2,  2,  5,  3,  3,  2,  4,  3,  4,  4,  6, 12, 17])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_dist = np.asarray(list(dict(karate.degree()).values()))\n",
    "degree_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.172452Z",
     "start_time": "2019-03-27T21:22:42.167521Z"
    }
   },
   "outputs": [],
   "source": [
    "conf_rg = nx.configuration_model(degree_dist, seed=np.random.randint(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.180684Z",
     "start_time": "2019-03-27T21:22:42.175398Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: MultiGraph\n",
      "Number of nodes: 34\n",
      "Number of edges: 78\n",
      "Average degree:   4.5882\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(conf_rg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: a `MultiGraph` can contain different types of edges and self loops. We convert it back to `Graph`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.191672Z",
     "start_time": "2019-03-27T21:22:42.184574Z"
    }
   },
   "outputs": [],
   "source": [
    "conf_rg = nx.Graph(conf_rg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.203372Z",
     "start_time": "2019-03-27T21:22:42.196083Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: \n",
      "Type: Graph\n",
      "Number of nodes: 34\n",
      "Number of edges: 71\n",
      "Average degree:   4.1765\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(conf_rg))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### More..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we mentioned above, we can leverage ___NetworkX___'s built-in datasets as toy data for practice or even in your research or even in your research or even in your research or even in your research. See <a href='https://networkx.github.io/documentation/stable/reference/generators.html' target='_blank'>here</a> for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Personally, I don't like ___NetworkX___'s visualization. I suggest using: (i) <a href='http://igraph.org/r/' target='_blank'>iGraph in R</a>; (i) <a href='https://graph-tool.skewed.de/static/doc/draw.html' target='_blank'>graph-tool</a>; (iii) <a href='https://gephi.org/' target='_blank'>Gephi</a>, which are also easy to use and actually prettier. That said, we can still use __NetworkX__'s `draw` function to have a first taste of what our data looks like. Let's also use ___karate___ data for an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we will need ___Matplotlib___ for visualization using ___NetworkX___. If you have not installed it, run `pip install matplotlib` in your terminal. If you use __Anaconda__, you are good to go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.727076Z",
     "start_time": "2019-03-27T21:22:42.206417Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt # import the dependent library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following line is a notebook magic command to draw figures ___inline___."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.740134Z",
     "start_time": "2019-03-27T21:22:42.730486Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "every time we run this, it'll show us a different layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:42.958058Z",
     "start_time": "2019-03-27T21:22:42.742492Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(karate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not bad, right? But! We want to make it better, start by adding node labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:43.270450Z",
     "start_time": "2019-03-27T21:22:42.960382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(karate, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can choose what layouts we want: consult <a href='https://networkx.github.io/documentation/stable/reference/drawing.html' target='_blank'>here</a> for more information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.247385Z",
     "start_time": "2019-03-27T21:22:43.274489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "# plt.subplot() will create \"sub-plots\" in one canvas\n",
    "# Now we are telling matplotlib to give us a 2x2 subplot canvas\n",
    "plt.subplot(221)  # first subplot\n",
    "nx.draw_spring(karate, with_labels=True)\n",
    "plt.subplot(222) # second subplot\n",
    "nx.draw_spectral(karate, with_labels=True)\n",
    "plt.subplot(223) # third subplot\n",
    "nx.draw_circular(karate, with_labels=True)\n",
    "plt.subplot(224) # last subplot\n",
    "nx.draw_kamada_kawai(karate, with_labels=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Network statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many useful functions to calculate network stats for you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Global level stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.258176Z",
     "start_time": "2019-03-27T21:22:44.250211Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Density: 0.13903743315508021\n"
     ]
    }
   ],
   "source": [
    "print('Density:', nx.density(karate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.280035Z",
     "start_time": "2019-03-27T21:22:44.260816Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. shortest path length: 2.408199643493761\n"
     ]
    }
   ],
   "source": [
    "print('Avg. shortest path length:', nx.average_shortest_path_length(karate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.300416Z",
     "start_time": "2019-03-27T21:22:44.288125Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. clustering coefficient: 0.5706384782076823\n"
     ]
    }
   ],
   "source": [
    "print('Avg. clustering coefficient:', nx.average_clustering(karate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.319097Z",
     "start_time": "2019-03-27T21:22:44.308559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Assortativity -0.47561309768461457\n"
     ]
    }
   ],
   "source": [
    "print('Assortativity', nx.degree_assortativity_coefficient(karate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combining these with [Section 2.2](#sec22), we can play with some comparison between the synthesized and real network stats. A common routine is that we repeat the construction of random graphs over and over again to obtain the average values to decrease bias from the randomness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `list` to save the stats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.326947Z",
     "start_time": "2019-03-27T21:22:44.322048Z"
    }
   },
   "outputs": [],
   "source": [
    "erdos_assortativity_list = list()\n",
    "conf_assortativity_list = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.334160Z",
     "start_time": "2019-03-27T21:22:44.330010Z"
    }
   },
   "outputs": [],
   "source": [
    "n = karate.number_of_nodes() # number of nodes\n",
    "p = 2.*karate.number_of_edges()/(n*n-1) # probability of edge creation (This is essentially the DENSITY of a network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.342542Z",
     "start_time": "2019-03-27T21:22:44.336754Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16,  9, 10,  6,  3,  4,  4,  4,  5,  2,  3,  1,  2,  5,  2,  2,  2,\n",
       "        2,  2,  3,  2,  2,  2,  5,  3,  3,  2,  4,  3,  4,  4,  6, 12, 17])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "degree_dist = np.asarray(list(dict(karate.degree()).values()))\n",
    "degree_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.740032Z",
     "start_time": "2019-03-27T21:22:44.345653Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in range(100): # randomly sample random graphs 100 times\n",
    "    erdos_rg = nx.erdos_renyi_graph(n, p, seed=np.random.randint(10))\n",
    "    conf_rg = nx.Graph(nx.configuration_model(degree_dist, seed=np.random.randint(10))) # convert `MultiGraph` to `Graph`\n",
    "    # calculate assortativity\n",
    "    erdos_assortativity_list.append(nx.degree_assortativity_coefficient(erdos_rg))\n",
    "    conf_assortativity_list.append(nx.degree_assortativity_coefficient(conf_rg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.751034Z",
     "start_time": "2019-03-27T21:22:44.742282Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erdos-Renyi assortativity: -0.0399590159833851 0.06704976562642845\n",
      "Conf. model assortativity: -0.1350366955396213 0.08130213975161112\n",
      "Real network value: -0.47561309768461457\n"
     ]
    }
   ],
   "source": [
    "print('Erdos-Renyi assortativity:', np.mean(erdos_assortativity_list), np.std(erdos_assortativity_list, ddof=1))\n",
    "print('Conf. model assortativity:', np.mean(conf_assortativity_list), np.std(conf_assortativity_list, ddof=1))\n",
    "print('Real network value:', nx.degree_assortativity_coefficient(karate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you see that there's a much higher tendency of the low degree nodes (mostly members) in ___karate club network___ to connect with high degree nodes (mostly leaders). We can then interpret that there may be a core-periphery or leader-member structure. (_We cannot be sure about this from this very simple approach though!_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Local level"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at micro-level network stats by giving some nodes of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.758854Z",
     "start_time": "2019-03-27T21:22:44.753879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "karate.degree(1) # degree of one node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.768589Z",
     "start_time": "2019-03-27T21:22:44.761469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DegreeView({1: 9, 2: 10})"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "karate.degree([1,2]) # degree of a list of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.778374Z",
     "start_time": "2019-03-27T21:22:44.771776Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 9]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path(karate, 1, 9) # one of the shortest path from node 1 to node 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are definitely a lot more to explore. In closing, I list a few topics that may be of interest but may not be covered due to time limit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P.S.: <a href='https://icon.colorado.edu/#!/networks' target='_blank'>Index of Complex Networks</a> is a good place to find more network data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### PageRank"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ranking nodes based on various strategies is a common thing in measuring node importance. PageRank is one of the most successful one and has still been used in both the industry and academia. Usually, PageRank is applied in a __directed network__, where an edge has a direction. For example, webpage ___A___ has a link to webpage ___B___ but not the other way around. Therefore in the network of WWW, we only have $A\\rightarrow B$ but NOT $B\\rightarrow A$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference: \n",
    "> Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry (1999) The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Build network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Network data from http://tuvalu.santafe.edu/~aaronc/datacode.htm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.791352Z",
     "start_time": "2019-03-27T21:22:44.780731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['1', '5'],\n",
       "       ['2', '5'],\n",
       "       ['3', '5'],\n",
       "       ['4', '5'],\n",
       "       ['4', '7']], dtype='<U2')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_list = np.loadtxt('sample-data/terrorists/terrorist.pairs', dtype=str)\n",
    "edge_list[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.800233Z",
     "start_time": "2019-03-27T21:22:44.793540Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Samir_Kashk'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_name_dict = dict(np.loadtxt('sample-data/terrorists/terrorist.names', dtype=str))\n",
    "node_name_dict['1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Map names to name indices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.810470Z",
     "start_time": "2019-03-27T21:22:44.802803Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Samir_Kashk', 'Essid_Sami_Ben_Khemais'),\n",
       " ('Madjid_Sahoune', 'Essid_Sami_Ben_Khemais'),\n",
       " ('Fahid_al_Shakri', 'Essid_Sami_Ben_Khemais'),\n",
       " ('Lased_Ben_Heni', 'Essid_Sami_Ben_Khemais'),\n",
       " ('Lased_Ben_Heni', 'Mohamed_Bensakhria'),\n",
       " ('Essid_Sami_Ben_Khemais', 'Samir_Kashk'),\n",
       " ('Essid_Sami_Ben_Khemais', 'Madjid_Sahoune'),\n",
       " ('Essid_Sami_Ben_Khemais', 'Fahid_al_Shakri'),\n",
       " ('Essid_Sami_Ben_Khemais', 'Lased_Ben_Heni'),\n",
       " ('Essid_Sami_Ben_Khemais', 'Seifallah_ben_Hassine')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_list = [(node_name_dict[source], node_name_dict[target]) for source, target in edge_list]\n",
    "edge_list[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.820133Z",
     "start_time": "2019-03-27T21:22:44.813248Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: terrorist network 911\n",
      "Type: DiGraph\n",
      "Number of nodes: 62\n",
      "Number of edges: 304\n",
      "Average in degree:   4.9032\n",
      "Average out degree:   4.9032\n"
     ]
    }
   ],
   "source": [
    "terrorist_net = nx.DiGraph()\n",
    "terrorist_net.name = 'terrorist network 911'\n",
    "terrorist_net.add_edges_from(edge_list)\n",
    "print(nx.info(terrorist_net))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.829261Z",
     "start_time": "2019-03-27T21:22:44.822917Z"
    }
   },
   "outputs": [],
   "source": [
    "nx.write_gml(terrorist_net, 'sample-data/terrorists/terrorist.gml')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Run PageRank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.857584Z",
     "start_time": "2019-03-27T21:22:44.831831Z"
    }
   },
   "outputs": [],
   "source": [
    "G = nx.read_gml('sample-data/terrorists/terrorist.gml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.864748Z",
     "start_time": "2019-03-27T21:22:44.859890Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: terrorist network 911\n",
      "Type: DiGraph\n",
      "Number of nodes: 62\n",
      "Number of edges: 304\n",
      "Average in degree:   4.9032\n",
      "Average out degree:   4.9032\n"
     ]
    }
   ],
   "source": [
    "print(nx.info(G))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$alpha=0.85$ is a commonly used dampling factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.890132Z",
     "start_time": "2019-03-27T21:22:44.867432Z"
    }
   },
   "outputs": [],
   "source": [
    "pr = nx.pagerank(G, alpha=0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-12T23:32:41.327959Z",
     "start_time": "2017-11-12T23:32:41.315825Z"
    }
   },
   "source": [
    "The returned value is a dictionary containing nodes and their __PageRank__ scores. Let's print them from high to low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.929560Z",
     "start_time": "2019-03-27T21:22:44.892243Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Samir_Kashk</td>\n",
       "      <td>0.006114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Essid_Sami_Ben_Khemais</td>\n",
       "      <td>0.047815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Madjid_Sahoune</td>\n",
       "      <td>0.006114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fahid_al_Shakri</td>\n",
       "      <td>0.006114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lased_Ben_Heni</td>\n",
       "      <td>0.009873</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     name     value\n",
       "0             Samir_Kashk  0.006114\n",
       "1  Essid_Sami_Ben_Khemais  0.047815\n",
       "2          Madjid_Sahoune  0.006114\n",
       "3         Fahid_al_Shakri  0.006114\n",
       "4          Lased_Ben_Heni  0.009873"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df = pd.DataFrame([{'name': k, 'value': v} for k, v in pr.items()])\n",
    "pr_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.946511Z",
     "start_time": "2019-03-27T21:22:44.932816Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mohamed_Atta</td>\n",
       "      <td>0.060755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Essid_Sami_Ben_Khemais</td>\n",
       "      <td>0.047815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Marwan_Al-Shehhi</td>\n",
       "      <td>0.044991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Djamal_Beghal</td>\n",
       "      <td>0.036374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Hani_Hanjour</td>\n",
       "      <td>0.035418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Nawaf_Alhazmi</td>\n",
       "      <td>0.032473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Ramzi_Bin_al-Shibh</td>\n",
       "      <td>0.027930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Zacarias_Moussaoui</td>\n",
       "      <td>0.026033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>\u0003Tarek_Maaroufi</td>\n",
       "      <td>0.024306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ziad_Jarrah</td>\n",
       "      <td>0.023493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name     value\n",
       "11            Mohamed_Atta  0.060755\n",
       "1   Essid_Sami_Ben_Khemais  0.047815\n",
       "31        Marwan_Al-Shehhi  0.044991\n",
       "17           Djamal_Beghal  0.036374\n",
       "43            Hani_Hanjour  0.035418\n",
       "44           Nawaf_Alhazmi  0.032473\n",
       "24      Ramzi_Bin_al-Shibh  0.027930\n",
       "15      Zacarias_Moussaoui  0.026033\n",
       "9          \u0003Tarek_Maaroufi  0.024306\n",
       "30             Ziad_Jarrah  0.023493"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pr_df.sort_values('value', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Community detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naturally, we want to know if there are groups/clusters of nodes in a network. Well seperated networks may indicate some emergent patterns within the nodes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we will again use ___karate___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:44.954666Z",
     "start_time": "2019-03-27T21:22:44.950449Z"
    }
   },
   "outputs": [],
   "source": [
    "karate = nx.karate_club_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several algorithms available in ___NetworkX___. Let us try the famous\n",
    "<a href='https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.community.centrality.girvan_newman.html#networkx.algorithms.community.centrality.girvan_newman' target='_blank'>Girvan–Newman method</a>.\n",
    "\n",
    "Reference:\n",
    "> Girvan M. and Newman M. E. J., Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99, 7821–7826 (2002)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:45.209371Z",
     "start_time": "2019-03-27T21:22:44.957978Z"
    }
   },
   "outputs": [],
   "source": [
    "comm = list(nx.community.girvan_newman(karate))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a hierarchical partition. Let's get the 1st partition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:45.223580Z",
     "start_time": "2019-03-27T21:22:45.214216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0, 1, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21},\n",
       " {2, 8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33})"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-12T23:48:52.727197Z",
     "start_time": "2017-11-12T23:48:52.721984Z"
    }
   },
   "source": [
    "We can now update our visualization by adding colors based on community partition. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:45.241117Z",
     "start_time": "2019-03-27T21:22:45.226433Z"
    }
   },
   "outputs": [],
   "source": [
    "layout = nx.fruchterman_reingold_layout(karate) # positions for all nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can realize this by plotting nodes seperately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-27T21:22:45.489650Z",
     "start_time": "2019-03-27T21:22:45.243382Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1340f0128>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MhQjHZWFCM5ca5oEBLv/s59T84G/p2Lv/tpvPuNO/5q//hss/+znmgbu7qv1sjvFxXKmmq8ldhrRxg1PXOkvf0BCmmCiX5hAVdwXuQAgH7gvNHGi+7qYVuZdxp3/38RNzf7u+5fSvfe31u1o8uru7ZwzFnUjKiy/g56Cj2CMpibxF9Gm1trby0UcfUVpaiveKgiXTc0TwxUVsVeG+0Myl6nT8Ijr9u7q6kKS5tw7Vvr4MPfwQQwMDaJqvzyqsNkCzbBl5r3x/waPobDYbjY2NVFZWYjQaKSgoYMuWLXh4eHC5rt65+loTeo4IBK4ghIN7u83nF83pP858tqoAjEYj5y5e5Ks//juUbR3T1pJS+mnpCgwg+bFHybz/voVcNlarlStXrlBZWYlCoaCgoIDU1FSUys83B5zpOeKXlUnKiy8sxJIFX0CEcOAep+OohwefnjpFZGcHMTExREdHT1vqYrH5ojj9JzIyMsLAwAABAQFzji0vLycvL29sbEDAbbWk+odH2Fd1lrX33UdaWtqCrXl0dJTa2lrOnj2Ln58fq1evJj4+ftpkQEd7johEVYG7EcKBe0IzozesI+3BB2htbeXChQvs27cPPz8/oqOj7Q/fRf7gflEzjXt7ewkMDJyzvH59fT09PT1s2bJl0vHxWlLd3d3sLS1l7ebNCyYaQ0NDVFdXU1VVRWRkJA899BCRkZFzXjex54ijFXcFAldxWTgkSSoEfgWkMdY3/BuyLF+dMkYB6JnsjP9vWZZflCRJCfwT8HXACvyzLMs/cXVdjuJKaCYKBeGbN+MfFUVUVBQrVqzAarXS2dlJS0sLly9f5uDBg/j4+NitkejoaKeTxOaLO53+d1OmcVdX15zbVCMjIxw6dIjNmzejVt/+Mejp6aG0tJQ1a9bMy1fiKEajkXPnznHp0iUSExN58skn57W1NhVXKu4KBM7iknBIkqQBSoGXgR3AXwPvAlPTdZMBkyzL4dNM8z1gNSABOmC/JEmXZFkudWVtjjIemukup6NSqSQiIsJe8M5qtdLV1UVLSwuyLFNeXo5GoyE6OtouJn5+fm55L+Pc607/mZiPf+PMmTNERUVNm4fR29tLaWkpJSUlpKe7N6elp6eHyspK6uvrycjI4Nlnn3XL333cShIIFgNXLY6NgEGW5bcAJEl6DXhJkqRlsixXTxiXB5yfYY5ngV/IstwFdEmS9AZj1seiCgcsrNNRqVQSFhZGWFgYy5cvx2az0dXVxY0bN6irq+PQoUN4enraRSQmJgZ/f39X3s497fSfje7ubmJjZ05y6+rq4sKFCzz33HO3nevr66O0tJTCwkIyMzPdtqb29nYqKipobW0lNzeX559/Ho1G47b5BYLFxFXhSAcujz+RZdkiSdK1W8enCkeYJEk1QCjwKfAXsiwbps4ByMC3XFyXUyym01GhUBAaGkpoaCh5eXn2pkM3btygsbGRo0ePolar7dtaMTEx6HS6GSunTodb6jH5ae+6TOPZcjhsNhtlZWUUFxff5nPS6/WUlpaycuVKsrOzXV6HzWajqamJyspK9Ho9y5cv54EHHsDT09PluQWCO4mrwuELDE45NghMLbQzDBwD/u7W818DbwB/MM0c010/CUmSvg18e+IxrVar2r59uyNrn5Y75XRUKBSEhIQQEhJCbm4uNpuN3t5eWlpaaGlp4eTJkygUiknO9sDAwFmFxB1O/+7AAD747DNyc3NJTk6eFBa6FDGZTIyOjs7oP6qtrcVisdwmDAaDgZ07d1JQUEBOTo5La7BarVy9epXKykqsVisrVqwgNTV1Tme9QHC34KpwDAJT9zF8gEm1N2RZ/oeJzyVJ+iFwcIY5brt+KrIsbwcmqURlZaUO6JvvwufiTjsdFQoFQUFBBAUFsWzZMmw2G319fdy4cYMbN25w5swZLBbLJB9JUFAQCoViUgFDn4R4VxbB6u/8CZ1qFVVVVRw+fJicnByys7OnLcK3FBj3b0wnqCaTiWPHjrF169ZJAmg0Gnn//ffJz89n2bJlTr+22WymtraWyspKtFotxcXFJCQkOGQlCgR3A64Kx2UmbCtJkqQCUhjbbmLC8b8C9siyPO7n8GLMChmfI43PfSDS1OvvJEvF6ahQKAgMDCQwMJDs7GxsNhsGg4EbN27Q0tJCZWUl6o4Oonv1eDVfh8EJDm21Gsxmh19TW5BPYFYmgYAkSdy8eZPz58/zm9/8hsTERHJzcxe0250zzOYYP3bsGGlpaZPauo6LRm5u7owl2OdieHiYmpoazp07R3h4OA8++CBRUa7VlBIIljKuCkcZECxJ0h8CbwOvAE3AhSnjJOB+SZK2AR7AT4C3bp3bAbwsSdJhxratXgT+0sV13fMoFAp0Oh06nY60+Pgxv8zJiun9Mk6IhjoxgdOBAQS3tNjLhoeGhnLfffexZs0aLly4wO7du/H29iYvL4+UlJRpw1oXm5n8G62trTQ1NU1yiPf397Nz506ys7NZvny5w6/V399PVVUVFy9eJCEhgSeeeGJe9bEEgrsdlz7psiybJEl6mLE8jl8yZjU8JcuyTZKki8BPZFl+G3iJMZ9GHWO5HDuAH9ya5pdAJGM5ICrG8jh2urKuLxIOdS2cDxOc/pHd3Xz66ads2LBhUgKcRqNhxYoVLF++nMbGRs6fP8+RI0fIysoiJyfH7WHFjtDV1XVbsp7FYuHgwYOsXbsWLy8vAAYGBti5cyeZmZmsWLHCodfo7e3l7Nmz1NXVIUkSzzzzjMsRcALB3YTC5mw5iiXGuI8jLy/vC+WEvPyznzuVe2JVKFBO+NsrtVpCSooI37RpktP/5s2bfPTRR+Tn58/6rbynp4fz589z5coVYmNjyc3NJSoqalH39202G7/61a/4xje+MancS0VFBS0tLTz++OMoFAoGBwd5//33SUtLo7CwcN7zd3R0UFlZyfXr11m2bBm5ublL1tcjEMwXi8VCVVUVQEBBQYF+Ptfc+b0FgdO4UsBQCUR/83n6lQq6DEZah4epGh4i4nItMf0GoqOjCQ8PJzQ0lG3btvHhhx/S39/P2rVrpxWDoKAgNm7cSElJCZcvX+bAgQOoVCqWLVuGJEmLEoLa39+PWq2eJBpGo5HKykq++tWvolAoMJlMlJaWkpKSMi/RsNlsXL9+nYqKCnp7e8nPz+e+++4TIbWCLzRCOO5iXC1gaG5pJftPv2M/ZDKZaG1tpaWlhfLycvr6+ggPDycmJoaSkhIqKir47LPPuP/++2f0Z3h5eZGbm8uyZctobm7m/PnzHD9+nMzMTJYtW4ZOp3NuvfOgq6vrNh/DxCKGQ0NDlJaWkpCQQFHR7A2crFYr165do6KigtHRUVasWIEkSV8oa1YgmAkhHHcpC1HA0Nvbm+TkZJKTk4GxaKFxITlz5ow947q6uponn3yS+Pj4GVuzKhQK4uPjiY+PR6/XU1NTw44dO4iMjGTZsmUzVn516f1MiaiaWMRwaGiInTt3EhcXR0lJyYyvbTabuXz5MpWVlWg0GlatWkVSUpIIqRUIJiCE4y5lMQoYenl5kZiYSGJiIjAmJC0tLezZs4d/+7d/IzY2ltjYWKKiooiJiSEyMnLaLRydTseaNWsoLCxElmWOHz/OoUOHyM3NJSMjw+6wduo9TMhZ6a25QETOWGLfxCKGFouF0tJSYmJiWL169bQiMB5SW1VVRUhICJs3byY6OloIhkAwDUI47lLcVcCwpqISncVMYGAgOp0OrVY7483Sy8uL5ORkXnjhBc6ePUtlZSUpKSkMDg5y+vRpOjs7CQkJISYmhqhblYInioKHhwfZ2dlkZWXR1tbG+fPnOXXqFGlpaSxbtsyh6rCG2kt0lpXTffK0XUB9gP79ZdSVFNMRGkRkZCTh4eF88MEHREZGTuufGRgYoKqqigsXLhAfH8/jjz9OaGio479IgeALhBAOB5n4DVel0eAbH39Hyle7q4Chh68PbW1tXL58mb6+PkZGRggICECn0xEQEGB/TBSV8c50Wq2WQ4cO8aUvfYnVq1czOjpKe3u7PSFx9+7dBAUF2bPbx4VEoVDYhcVoNHLhwgV27txJcHAwubm5JCYmzljaxDwwMGstMWt/Px1792EDolYs56OuLsKio1m/fv0k0dDr9Zw9e5YrV66QlpbG008/vaD+F4HgXkKE486T6b7hwsTaVRvwz3BvCe7ZMPf3U/nC91zarlL7+1Pw5r9OEr7h4WH0ej19fX3o9Xp6e3vtz6cTFZPJxMmTJ3nggQdu61thNptpb2+3Z7d3dHQQEBAwqUzKeIVYi8VCXV0dVVVVDA4OkpOTQ1ZW1qQIKWdyVixRkRT/4+t43HqPN2/epLKykqamJrKzs8nLy1v0BlsCwVLCmXBcIRxzMNc3XDt3oEVn3b+96VIBw/AH7idlQlTVXEwUlfGHXq+npaWFmpoaUlNTycnJsW97jYuLr68vCoUCi8VCR0eHXUja2trQ6XSTCjf6+PjQ0dHB+fPnqa+vJzk5mdzcXMLCwpzOWQkuKUb7taepqKigq6uLvLw8cnJyXPKtCAT3CiKPw8049A3XZqP7+AlG9HoyX31lUcTD1a6FYZs2OnSJl5eXvafIVLq6unjnnXfQ6/WEhobS2tpKbW0tfX19mM3mSUKi0+lYtWoVWq2WgYEB+9j9+/ej1WqJiYkhISGB/Px8mpqa2LVrF0GDJkKdzFnpOn6CKrWK7Afv55FHHlkSpVEEgrsZ8Qmahbo33nS4lIfxYi11b7xJ+svfX6BVfY67uxa6QkhICN/85jfZtWsX3d3dPPDAA/Yb9PDw8CQL5caNG5NEZVxMxos3Dg4OUlVVRXd3N76+vsTGxuJx8JDTOSsKoECjIdUNPTYEAoEQjhlxJSu7+8RJDJdkt96YZ8K5roUZ8+pa6CgajYatW7eyd+9ePvjgAx555BE0Gg1eXl6Eh4cTHn575+CpotLb24vBYKCvrw+r1crg4CB1Fy+SfuXqNK84f3pOnsb8/NdFH26BwA0I4ZgBV7OyOw+WLYpwONq1cCguDu7bvGBbaWq1mi1btnD48GHeffddtm7dOqnooc1mw2azYbFYGDEaGWxqxjowiL+XB6GxsShSU7FarXbLQ6/Xo79Qi8KJCr8TmStnRSAQzB8hHNOwEFnZjmCz2bBarVgslhl/Hr+5jv/s99wzKAtXMnDyNKaaGmz9A59P6OuDR2YG6txcVKHB7NmzB/2QidDQ0ElzOfqac13T3NzMSy+9RHp6OhqNxn5cazAQ0taBrrMLj9HRz3/vnp70R0XQHx/HaFgoSqUSpVKJpr0dd2RWWEzuyX0RCL7oCOGYBndlZX/y698wHBqC1Wqd9Jjt5jwe5aZQKFAqlahUqml/nvGclIIqOQF1rx6VxYLCywNbcDBKH5+x60wmMjMzOXbsGJs3b8bb2xuVSnXb3O56XLt2jaNHj7JlyxaigoNpePPf6a48P61lpB4ZIaCxmYCm65Mi1PQXa7lQfsSlvweAyts9uS/jLJWcHoFgsRHCMQ3uysrOSElBm5M97Q11NkFYjL7ekZGR9PT0sHHjxgUtq7Fs2TKCgoLY/cGHLKurx9zQOPdFUyLUfOPjUPv7YTYYnV6H2cuL2s5OUsNCZ2wtO1+WWk6PQLDYCOGYBndlZYdGRRG4RFuIFhcX8+6773L+/HmnW6bOl5iYGFb19NE/H9GYgPFiLZf/5V+5WVJIm1KBC93TCVhZgF6p4OOPP0apVNqLOUZERMxbRObK6TEbDHTs3UfHvv2LntMjECwmQjimYewbrr9L21UWLy/eKTtIZEM9CQkJJCQkLKkucSqVii1btvD73/+eqKioaXMz3IWh9hL9Z885dW3v6TOcbLtB3prV8MlnTgUs2IDr/n6UZGezdu1abt68SV1dHfv372dkZISkpCRSUlKIjo6etdTJUs7pEQgWE5eFQ5KkQsZax6Yx1v71G7IsX50yxhv4Z2Drrdf8FPgzWZb7JElSAHrGeguN89+yLL/o6tqcRa3VEly0yqWs7Kj161j5zedpbm6msbGREydO4OPjYxeRqKioO97bQafTsX79enbv3s0zzzyzYM2JXIlQUwJbU1LJ+OM/orarh96TpxyeI7ikCO8VBbz77rs/uKX7AAAgAElEQVRkZmayatUqSkpKKCkpobu7m/r6eo4ePYrBYLCLSGxs7KREwaWe0yMQLCYuCYckSRqgFHiZsT7ifw28C0zd+/gRkAhkABbgLeAXwLeAZMAky/LtQf53EHdkZXt7eyNJEpIkYbVa6ejooKmpiWPHjtHb22vPkE5ISLhjfbolSaK5uZny8nIeeOABt8/vjgg1Q+U5TL19XE6II7i5CXVr+7yv7Q8MJO7JJ8hISSE9PZ3jx4/z1ltvUVJSQkZGBsHBwQQHB7Ny5UoMBgPXrl2jsrKSPXv2EB8fT3JyMkGm4bsip0cgWCxctTg2AgZZlt8CkCTpNeAlSZKWybJcPWGcBvgHWZZ7b437D+D/u3UuDzjv4jrcjruzspVKJZGRkURGRlJUVMTg4CBNTU00NTVx/PhxfH197SISGRm5qNbI+vXr2bFjB7Is31ao0FXcFaH28X/9Jx6JieT+6Ifc+D//Td/pM/OqHRb52CPsPniQx/38CA8P5/7776etrY1Dhw5RU1PD+vXriYiIAMDf35/8/Hzy8/MZGBigvr6e2tpalPsOEHwX5PQIBIuFq8KRDlwefyLLskWSpGu3jldPOP7nU657DKi59XMeECZJUg0Qytg21l/Isuza3cYNOJeVnTmvrGwfHx8yMjLIyMjAarXS3t5OU1MTR44cQa/X262R+Pj4BbdGPD092bJlC6WlpYSHhxMQEOC2ud0VodbS0IhOp+OdDz/EHOCH5/JcQts70N3swmPk81wQi5cXw/FxjEqpGGOiUd9owc/Pj1/+8pds2LCB4OBg1Gq1vbXtb37zG+Li4igsLMTf3x8PDw/UajWenp5kZ2eTkZhIxXsfYHFh7a7k9AgESxFXhcMXGJxybJCxnjrTIknSnwNPAuNNn4eBY8Df3Xr+a+AN4A9cXJvLOJqV7WwkjVKptPenKC4uZmBgwG6NHDt2DD8/P+Lj4+3WyEKE64aGhrJq1So+++wztm3b5jaLx10Rat/8zp8QkJVlf26z2TCbzQzp9dSdPMkH77zL83/8R3hERmL18GB0dBSz2czo6CjBwcF4enpy6NAhiouL0Wg0mM1mzGYziYmJXL16lUOHDhETE0N4eDgWiwWz2YzFYsHPYCDN6HwYMIisdcG9h6vCMQhMvTP4AP1TB95ygv+MMUG4T5blKwCyLP/DlHE/BA7O9qKSJH0b+PbEY1qtVrV9+3ZH1z8nal9f0l/+PoZLMp0Hy+g+eWqG2P2NbtuO8PX1JTMzk8zMTLs10tDQwKFDhzAYDMTFxdn7eWvd+C02NzeX5uZmTpw4wZo1a+zHXUl0c0eEmtrfH2385GBchUKBh4cHHiEh+KWn06PzJ7aoaMYe6Pn5+aSnp3Pq1Cmeeuqp2yLcenp6OHz4MAaDgeLiYnQ6HX19fXSdqcB2xrmIsImIrHXBvYSrwnGZMQc3AJIkqYAUQJ44SJIkNfBbIAcokmW5ccK5vwL2yLI87ufwYswKmRFZlrcDk1RivB+Hs29kLvwzJPwzJBKef46B5utYTCZU3t74xsUu6BbERGtk9erV9Pf309TURGNjI0eOHMHf39/uG4mIiHDJGlEoFNx///387ne/IzY2lsCBQZcT3boHBugJDsLfBeEIKlw56+/YYrGgVCqxWq2zzpOVlYXZbOa9997j4YcfxmKx2PuLGAwGhoaGuHHjBq+//johISEUFBQQqVTiDvvO3VnrAsGdxFXhKAOCJUn6Q+Bt4BWgCbgwZdxPgUxgtSzLU2/uEnC/JEnbAA/gJ4xFXS1J1FrtHd1y0Gq1ZGVlkZWVhcViob29ncbGRsrLyzEajZOsEWc623l7e7N5zRou/vRn6No7nU506+vr48CBA5w5c4ZwtYKVjJU3dxQbUDk8hFWWSUtLmzZZz2azoVKpmNiUzGq10t/fb6+8O15xV6/Xc+3aNX74wx+yZs0aQkND0el0xMbGkpOTw6OPPoqnpydVVVWcPXuW6NRUVH5+WFzYrlL7++MbF+v09QLBUsMl4ZBl2SRJ0sOM5XH8krHoqKdkWbZJknSRMRF4F/izW5e0TIjauSHLsgS8xJhPo46xsP0dwA9cWdcXBZVKZe+ct3r1aoxGI01NTdTX13P48GF0Op3dwT5fa8Q8MIDh12+ha+uYewHTJLoNDg5SXl5OWVmZ3em+atUqmn75b05FqIWUFBP/lW0cPXqUs2fPsnr1auLi4sbWajaj1+tpbm6mr6+P8vJye5l2o9GIt7e3vYGUTqcjLS2NgIAAnnrqKc6ePUt9fT0bNmywt6+dyMqVK0lPT+fYsWOYA3UEuiAcwUWFwjEuuKcQrWPvUSwWC21tbTQ2NtLU1ER/f7/dwR4XF4ePz/TxC862Zw0sKqS9cAWffvopKpWKBx98kOLiYnvP8LHM69cdjlBL/v5LDJjN9Pb2cvHiRSoqKgAIDw9HpVLh5+eHwWDg9OnTvPDCC0RERKDT6dDpdLN2+rPZbBw5coTW1laeeOKJWdvI1h8+Qusv/sUpiwmFgpyfvibCcQVLFtFzXAjHjBiNRhobG2lsbKSlpYXAwEC7byQ8PByFQoGh9hI1P/hbp5IercChqHBWPbGVtWvX2gVjIvPt324Dbvj6cDkhltCYGLsQjPcvv3nzJleuXCE5OZmSkhKampr43e9+x6uvvupQ6LLNZqOsrIyenh4ee+yxWTPnL73+j/Q4kQQYXFIsMscFSxrRc1wwI35+fuTk5JCTk4PFYqG1tZXGxkb27dvH4OAgCQkJBFecdak0yFMZmaRPk31utVoxGo3o9XpGtjyANT6WkXNVqBsaUQ+PfD5O44VSkvAvKSQ8OZnufft46KGHSEpKum3ODRs2cO7cOXbs2IGHh4e9NL0jKBQKNm7cyN69e/nkk0949NFHZ7RSUr/3XWoNhgXJ6REI7jaExSHAYDBQf+kSxl/8/yiHZw1omxWln5aQV/4K48jwWOe+WxFLRqMRX19f/P39CQgIICAgAH9/f7yAU598SmiAjvxVhWjj4yb5AlpbW9m1axdPPfUUQUFB077m4OAgv/vd79i9ezff/e53Wbt2rcM1t6xWK5999hlms5mHH354xv8/87WYXMnpEQgWG7FVJYTDafQXa7nwg791eZ7ehx/ENzV1kkBM528YHh62Z6pv2LBhxtLmFy5coLKykqeffnpGP0RFRQXvvfcey5cvx2AwsGrVKrKyshz6f2CxWPjkk0/w8PDgwQcfnDWQYKacHpWfHyHFRW7N6REIFhqxVSVwGneVBikpWEFQwfJZx4yLRkREBOvXr5+1H0Z2djY3b97ks88+49FHH532hm6xWPDx8WHTpk1YLBaOHTvGuXPnKCkpISUlZV79NlQqFV/60pf46KOP2L9/P/fff/+M103N6em83sLugwfwjo1h2Yp84lNu31oTCO4lFr7VnOCuwF2lQZghc3ucoaEhdu7cSWRk5JyiMc66desYHR3lxInpo72sVqs9ATA8PJwvf/nLbNiwgTNnzvD73/+elpaWeS1drVbzyCOPoNfrKS8vZy5rfDynJ37jelRxsXzl61+nq6uL3/72t9TV1c15vUBwt6L60Y9+dKfX4Bba2to0wCuuZk9/UVH7eNOx/yBWF3wcQ0oFbzXWc7muDoPBwOjoKF5eXvYtpqGhIUpLS4mOjmbdunXz7rynVCpJTEzk0KFD+Pj4EBISMul885Ur6C9fIc7HF9WQCbWPD0FhYWRnZ9trVDU3NxMSEjJjGPI4KpWKlJQUzpw5g16vJzY2ds51qlQqqqurycnJITs7m5CQEI4dO8a1a9cIDQ2d8zUFgjuJzWajvb0d4PWoqKh53QDEVpUAcE/zqrDVq9m6ppiLFy9SVlaGUqlEoVAQHBxMfHw8siyTnp5OSUmJwz2/vb29eeSRR9i5cyeBgYGEhYXZe3/bDh8lb2iI9ksy7UwuiSJlpJOSkkJNTQ2lpaXExcVRXFw8azdGLy8vtm7dyvvvv49araaoqGjGseMEBATQ29uLn58fsbGxPPvss1RXV/P++++Tnp5OUVHRrLkiAsHdhLA4BHbUWi2dB8ucu1ihIO2F75Ccn8fy5ctZu3YtKSkpBAUF0dXVxd69e+1VZysqKmhpaaG3txez2YyXl9eMxQknMh6ZdXD3btR79tP81tsMXKtHYTZPGmcdHmbgWj2dB8oYbGomaHk+UXFxZGdn09PTw4EDBxgcHCQsLGzG1/Xw8CA5OZkjR45gsViImqN3fGtrKyqVivDw8Fu/DgURERFkZmbaG2V5enoSGhrqsGgKBAuJMxaHEA6BHa/QUAabmjFdn59PYCLBJcVEb33M/lylUhEYGEh4eDhNTU0UFRWxfv16PD09MRgMGI1Gent7aWxspLKykosXL9LR0cHg4CAqlQqNRjPtDVan0WD67f8wfOnybeemw3S9BcNlmeCiQjy9vYmNjSUjI4OWlhYOHjyIzWYjLCxs2ggsT09PkpOTKSsrmyQK09HT04PRaCR+ShVfDw8PkpKSiI2NpaKigurqaoKDg+9Yx0eBYCpCOIRwuExAfi6GyzIjN2/O+xq/rEyk77+Eckr+hMlkorS0lPj4eDZs2EB4eDhpaWmsWLGCuLg4NBoNJpMJk8mEt7c3VquVnp4eLly4wKlTp7h+/Tp6vR6LxYK3tzdqtZor//xLTA4k4QGM3LzJUFs7IWtKgDFBSExMJDk5GVmWOXbsGB4eHtNaA15eXiQlJbF//340Gg2hoaHTvobJZOL69eszdlAcL5Xv5eXFwYMHuXnzJhEREQvW510gmC9COIRwuIzS05PgokKG2toxtdyYfbBCQXBJMdL3X7ot0c1kMrFz504SEhJu82koFAr8/PyIiYkhOzubnJwc/Pz8GB4eprOzE5vNRlRUFFqtFrPZTF1dHUePHqXx2HFUR445VTPK1HKDgNxcvEI/d6x7e3uTmppKVFQU586do7KyEq1WS2Bg4KT1ajQaEhIS2Lt3L76+vrc552Hsw1ddXU1eXt4svy4FISEhZGdn09XVxYEDBwAICwsT/2cFdwwhHEI43ILS05OQNSUE5OaiUCoZvtk1KdpK7e9P6Pp1JP3xt4je+thtlsbg4CClpaXTisZ0qNVqgoODSUpKIi8vj4SEBGw2G52dnTQ0NKDRaEhLSyPwsoytrd3p96VQKglateK241qtloyMDAICAjhx4gSXL18mMDBw0naSt7c3cXFxfPbZZwQGBhIYGDhpDk9PT8qOnMQrOJWGThNdhhF8NWq8PG7/v6hSqYiLiyMlJcVuXel0utvmFAgWA2eEQ2SOC+bE3N9P89lzHN23lzAfLSvWrsEjMGDaToCDg4Ps3LmTpKQkiouLXXYEm81m2traaJJlrNv/D6oJta0cRe3vT8Gb/zpriXObzYYsy5w4cYLg4GBKSkomWRjt7e189NFHPPjgg3Z/xvl6A7sqb/LZmVaGLZ8HKgb4qtmQE8TDK0LJTZw5iquhoYHDhw8TEBDAunXrhIAIFhVRckQIx4JgqL1E/ce70FdUoh4ZtR+f2glwXDSSk5MpKipya/SQu0qiZL729wRmZ885zmw2U11dTUVFBYmJiRQVFdktkBs3brBr1y423r+F/zxiorymB+ssHyOlAjbkBPHqV5Lx854+An78w1tRUUFWVhYrV64U4buCRUEIhxAOt+JIUT/dyhVURYSSkpU1r7wHR+mprOTSj3/i8jzXlufik5VJZGSk/TFdCfhxhoeHqaio4MKFC2RlZbFixQo0Gg21V+p5+dfX6Biaf4Om/CQ/fv7N9BnFA2BgYIBjx47R3NzM6tWrSU9PF+G7ggVF1KoSuI2xxks/xXjx0tyDbTb0p8+QGBvDiqefXpD1uKskSlRCAlZ/f3p7e7lx4wbd3d34+PhMEpLg4GD7zdrLy4vVq1eTm5vLqVOn+PWvf01BQQHv1vg6JBoA5+qN/OSda/z0+ZkLIPr6+vLAAw/Q1tbGoUOHqK6uZv369URERLj0vgUCdyKEQzAtdW+8OT/RmMj1FureeHNBGhf5xseh9vefVI3WURRaX3TJSfSZTPT399PT04OHhwcKhYLW1laamprseSQxMTFERkYSERFBREQEWq2WzZs3k5+fz45PT1N2YQRnuqiX1fRwvsEwq88DIDIykq9+9avU1tby8ccfk5iYSElJiShfIlgSuCwckiQVMtZzPA2oAr4hy/LVKWOUwD8BX2esWdw/y7L8k7nOCe4MhtpLY9tTTtB94iSGS7Lby4q7pSRKSQkpa9bYn9tsNoxGI93d3XR3d9PT00N3dzednZ3Isowsy5jNZkZGRggPDyc5OZm4uDg6iAPmZdHfhtUGn1TcnFM4YCx8Nysri5SUFE6dOsVbb73FypUryc3NFduxgjuKS8IhSZIGKAVeBnYAfw28C0wNZv8esBqQAB2wX5KkS7Isl85xTnAH6Cwrd7oTIDYbnQfLFqQfRdjGDXTsO+Dc2hQKwjZtnHJIgb+/P/7+/iQmJtqP22w2DAaDXUhu3rxJU1MTR44cwWgyc8b2ICg0Tr+P8poeXnw4Hn+f+X38vLy8WLduHVlZWRw+fJiLFy+ybt2627LUBYLFwlWLYyNgkGX5LQBJkl4DXpIkaZksy9UTxj0L/EKW5S6gS5KkNxizMErnOCdYZMz9/XSfPO3SHN0nT5Hw/HOzhr06g39mBsHFRXQfn768+mwEFxfNW8wUCoW9z/lUQTla08aJXzc5/PoT6RswU98+SF7S3FbHRIKDg9m6dSv19fUcPHiQkJAQ1q1bh06nc2k9AoGjuJoplw7YiwbJsmwBrt06PuM4QJ4wZrZzgkVmoKnZJT8CgNlgYKD5uptWNJmUF1/ALyvToWvc1ftboVCg9HCPk77XMOhUvw6FQkFycjJf//rXiYiIYMeOHRw/fpyREefzWwQCR3HV4vAFBqccGwSmevCmjps4ZrZz0yJJ0reBb088ptVqVdu3b5/fqgUz4q5OgBaTe+aZitrXl8xXX5lXmLANCCxchfTnL7qt97eXm8JJDpftQz45YM8Yn/qvRjP7VpharWblypWkp6dz7Ngxfvvb37J69WrS0tKWZPiuYdBMXdsAphEr3p5KUiJ9571VJ1h6uPqXGwSmfgXzAfrnGDdxzGznpkWW5e3AJJUYz+OY16oFM+KusFfVLLkRrqL29SX95e9juCTTsO8QladlLBbwso4SOXwTfz8NwUWFtAYF0BIUSJaLojE0NERjYyPXrl2jrvEGGnUmQ2bnndNaL/iTrz+GzteTvr4++6OhocH+s1KptPdtn/qYmBjo5+fHQw89xI0bNzh06BA1NTWsX79+xmKMi814Vn15TQ/6gc/L3883q16wNHFVOC4D3xp/IkmSCkhhbLtp6rg04Pz40AljZjsnWGTcEfaq9vfHNy7Wjau6nfP1BnZdUFPelY8+PMd+XOcFG7IDeaQ4iqJoDW+//TZNTU0OO5L1ej319fXU19fT2dlJdHQ0ycnJbNy4kf5P2vjwZKfTa5dCR/jgvf8hPDwcSZJITk6eJAY2mw2TyWQXkd7eXurq6tDr9fT19aFWq6cVlC9/+ctcuXKF0tJSUlNTKSoqmjW5cSExmsy89s61GbPq+wbMfHCyk49Odc6ZVS9Yerj6lyoDgiVJ+kPgbeAVoAm4MGXcDuBlSZIOM7Y19SLwl/M4J1hk3BH2GlxU6HbH+Dhz3ZD0w/BhZS8fn+1lQ04QzxWv4+DBgzz33HOzNosaL6p47do1GhoaGBgYIDExkfz8fGJjYydd+0hBKB+f6py1zMhMKBXwna35ZMaU0NDQgCzLHDp0iLi4OCRJIiEhAbVajY+PDz4+Prc1kLLZbAwMDEyyVK5cuUJfXx96vR5PT0/8/Pw4d+4cZWVlFBYWsnLlSgIDA+fVLMsdGE1m/vI/L1NVb5xzrNUGB6t76O0fnTOrXrB0cOmvJMuySZKkhxnL4/glY1bDU7Is2yRJugj8RJblt2+di2Qsz0PFWK7GzlvTzHZOcAdwJezVBhjjY7FarW6vUuzcDcmPh+IiOXXqFGsm5HDAWD2qlpYWGhoaqK+vR61W262K2aos5yb5syEniIPVPQ6/h405QfatmbS0NNLS0hgaGqKuro7z589z4MABkpKSkCSJmJiY29agUCjQarVotVpiYmImnbPZbPT399utlObmZk6cOMGePXuIiIggPDx8Wp9KQEAAavXkW4ErPonX3rk2r7/RROaTVS9YOohaVYJpufyznzsV9upbsJy6TInh4WHWrl1LXFyc29b0yq9lp27W6zN1pFqOsnXrVvz9/WlsbKS+vp7m5maCgoJISkoiKSmJoKCgec9pNJn5/n9e5pwDN8j51KoyGo1cuXIFWZYZHBwkNTWV9PR0wsLCnHJ622w2rl69ypEjR9DpdGRmZmI2m+nt7UWv19Pb24vRaMTHx4eAgAD6rEFc6PTmfIsN4/Dn94b5+iTO1xv4k/990Wlr7N//NEv4PBYZUeRQCIfbGKtV9TpGB7rt+WVlkvnqK6h8fOzNl4KDg1m7dq3LpcJduSEpgK/l3OT6xSMkJiYSExNDUlISiYmJ+LrgODeazPz972QO1+qZrfyIAkjQ9vGL76wkJvL2JlAz0dPTY89gVygUSJKEJElO/S5HR0eprKzk/Pnz5OWN9YUf37qyWq20dfby+s4mTl8bZrZfsQJYlezFy0/EERkWeNtn7bV3r7nk/9laFMYPtiU7fb3AcYRwCOFwK45Uxw0uLiLlxRcmhb2azWaqqqqorKwkPT2dwsLCOcNMZ8LVG1JBzAjLNLUsX76cwsJCp+eZyrFjx7jSPkqbNZbymh76pkQObcwJ4ksrQlEYG6mpqeErX/mKw74Gm81GR0cHsixz5coVtFotkiSRlpaG1kFfksFg4MiRI3R2drJ27VqSk5PpH7LMewtwnEifAdYFywQH+Nq3vLx8dPzNh8MYh5y/pwT4qnnvlXwRqruICOEQwrEgGC7JVL39NqprDTD4ecrN5/04Ns6alT04OMjJkyepq6tj1apV5OTkOPQ3MgyaefL1c5PCOR0lwFfNf3wnkd0fv88zzzyDv7/r2yGjo6P813/9F9u2bSMwMBDD4FhG+OCIBR9PFUkRPvYboM1mY8+ePajVau677z6nX9NqtdLS0oIsy9TX1xMSEoIkSaSmpjrUv6O5uZlDhw7h6+vL0a4UjsoDDq9lU04g/+vRULuT/ly9nu0nXY/i2v6nWQ5n1QucR5RVFywI/hkS5k0b8H/0YRL9/LGYTKi8vfGNi51X9JSPjw+bNm1i2bJlHD58mOrqatauXTupnMdsXGsfdEk0YCz8s2fIk+XLl1NWVsZjjz3mcqKcLMtERETYt478fdQz3vAUCgWbNm1ix44d1NbWkpnpWPb7OEqlkri4OOLi4hgdHaWxsRFZljly5AixsbH2yKy5rJq4uDieffZZdh6o4uixfpyp9Ft+oZdn1keRmzy2tTTk0wsnL89x1dwMjlhcnkOwsAjhEMwLq9WKyscHXWaG03OEhITwxBNP0NjYyOHDhzl//jxr1qyZ1Jp1OgaH3XMjGRyxULh8uX3LR5Kcj+Cx2WxUVVWxbt26eV/j6enJl770Jd5//33CwsLmfN9z4eHhQWpqKqmpqQwPD1NXV0dNTc2kyKzY2NgZo8NUKhVX9AGAc1uA45V+M2O86erqoq2lzYV38zk+nmLHYKkjhEMwLywWi1u2ABUKBYmJicTFxVFdXc3OnTtJTU2lsLBw2l4TBoOBtpYGl18Xxm5IKpWKzZs3s2vXLuLj4532uVy/PlaLKzbWsUTHkJAQ1q9fzyeffMLTTz/ttvawXl5eZGVlkZWVRX9/P1evXuXEiRPs3buX1NRUJEkiIiJikpVlGDRTXuN4lNpEPjvTiqZtLxEhOnx1Ifh4aBkcdd6SC/BVkxQheo4sdYRwCObEMGimoUdJn8LMkJfeLXWGVCoV+fn5pKen23tNrFixgqysLNrb22lqaqKpqYmhoSHCouLx8/LHOOz86028IUVGRpKamsqRI0e4//77nZqvqqqKvLw8p7a70tPTaW1t5cCBA2zZssXttaW0Wi35+fnk5+fT29uLLMvs3bsXq9Vqj8wKDg52yxbgkEXNyvWP4TV8g0uXLpHgF0ZtT4DT823ICRKO8RlYSvW+xF9IMCOT6wxpgQGg1q11hjQaDcuWLQPggw8+4M0332T58uUUFBTw4IMP2vMXLppci6qaekMqLi7m7bff5vr16w5bDb29vbS3t7Nlyxan17Nu3Treeecde3jsQhEYGEhRURGFhYX2BlWlpaX4+Pgwqk11y2u8/T/vsjLVj/T0dCLSQvnhe92zhvTOhFIBD69YGjW2lhJLsd6XEA7BbSx0naHh4WGuX79OU1MTzc3N2Gw24uPjef7557FarZw6dYqWlhaSkpLs38ZdLfMx9Ybk5eXFhg0bOHDgAF/72tccCpGtqqoiOzvbpRIearWahx9+mN///veEh4cTGRlpP7cQ3ywVCgXh4eGEh4ezZs0abty4wZ4TV7m9RqnjFK9aToxulL6+PkymNpJ0Oq7pHS85sybDn8yYO1NbaymylOt9CeEQTGIh6gzZbDZ7F72mpiY6OzuJjIwkPj6evLw8goKCJm3XpKSkcOHCBT744AMSExMpLi52W5mPiSQlJXHp0iVOnz7N6tWr5zXX8PAwV65c4Wtf+5rD65iKTqdj8+bN7N69m2eeeYYrbaOL8s1SqVQSGxvLV4Ij2VFdicFkdXouL+UoZmML6uBoMjIyiI6O5sGHVQ5n1UdpTSSaa/nVrw6iVCrx9vZGo9Gg0WjsP8/0r0ajsfeOv1dY6vW+RB6HYBLOlvXYvCxoUp0hk8lEc3MzjY2NNDc34+npSXx8PPHx8URHR+Pp6TnnnMPDw5w+fZra2rHEvZT0HF75TZ1DN6QITT8//YMksqTps5H7+/t5+/Xlc9MAACAASURBVO23efLJJ+cV5VRZWcnNmzd56KGH5r2Gudh38Ci/OTnK1R7NrBaVUoHbvlk2NDSwa9cudl32pVfjfKTc44WhfHOtLy0tLbS0tNDW1kZgYCAh4bF8dNmHM/XDc76njTlB/ODWe7LZbIyOjjI0NITJZGJoaMj+89TnE48DeHt74+3tjZeXl/3nmcRHo9Hg6em5ZMXGXZ/D+SDyOAQucb7e4HSUTVlNDwfO1ONj7qCpqYne3l6io6NJSEigqKjIqfamXl5erF27lpycHI4ePcqFCxd4YV0xO7RBM5rv44zfkL65LoayfbvxVFpJTb19T1+r1VJSUsKBAwfYtm3brIUZrVYr1dXVLvk2pmI0mXm3NgC5e+G/WZrNZk6ePMmePXtoa2ujsLCQ//X8Bv7mnQ6ntwAfWRlGZKQ/kZGRrFy5ErPZTEdHB9evX2d9WD3Bpn5aRqO4ptcyMPL5TXpiVv1EK0qhUODp6Ymnp6dDSZoTxWaqwPT19dHR0XHbOYvFcpu4zGXpLIbYuPo5PN9gWHCfhxAOgZ1dlTeduoHA2E3t7X1X+OY6LatXryYqKsptll9AQACPPPII169f58iRIxT4ePDgMys5UW+ZtsxHlOdN/uChDDatGEswDHjiCT744ANGR0enTbzLzs5GlmVqamrIzc2dcR319fX4+voSERHhlvcFtyrJNixsJdne3l727NnDkSNH8PLyYtOmTWzevBlvb29sNhu7qvo57kTm+HRbgGq1mujoaKKjowF4fHSUtrY2rtS3UH2tg65eI2FB/ixLCUNK1hAe/nnorSu+HQ8PDzw8PPDz85v3+s1m8yTLZaLYGAwGbt68eZvYmM3mWa2Y6cTHy8vLIbFx9XP4ScVNIRyCxcEdMf0tQ8EsW75wdYZiY2N5+umnqa2t5eTJ/RTExvKNF1fR2a+aVOajsa6WxsYaYEw4QkNDefLJJ+3iMVUcFAoFGzdu5L333iMpKWnGm894CK67WMhvluNVcXfv3k11dTVJSUl861vfIi8vD6VSidFopKKigkuXLpGutNARInGta/6vH+k9QCoNVFQYkSRpxt+Zh4eHPdP9vg0wMjJCa2srLS0tHD78f9s78/gor+vuf7UvI41WNm1oAV1JlpCFhMAGEwzBxAbHxMbQmDh23MRtarep27xt7bRp2iTunuTN1oakzpsNO8GxHZtgY2KEwcIIEAgEiAtIYtOC9tEy0iyaef8YzVi7ZtfC/X4+8xnNM3ce3bkz8/zuPefccw7T2dmJKTKVq32JVDcG0D3gekZedwkODiY6OtplsRnPXDYwMEBvby9tbW0jVj4GgwGTyURYWJhTYmOyhnDobLtH7+tQdQfPbl7s01BdJRwKwHtpPeqa9T7NMxQYGEh+fj5Lly7l5MmTvPX6bygsLGRFcbEjyikvL4/jx4/T2trqKKEaHx/Ptm3beO211zAajaxYsWLEeRMSEigsLKSsrIwHH3xwzAyxpaWF7u5ulixZ4rX34ouZpdFo5OjRoxw4cIDW1lZKS0v5+te/TlJSEiaTiUuXLlFTU0NLSwtZWVmsX7+epKQkdg4MThrBY8duAnz+0RK6O2yJF3fv3k1CQgLZ2dksXbp00qqDoaGhpKenk56eTk+/ma+/fIn3q3VD4bsj//FMrBIYHBzsqIfiLIODg+OuagYGBtDr9XR0dDiO1bZa0eldq1Y5Gn/8DpVwKADvpvXwB2FhYaxevZqCggLKy8v5+c9/zurVqxFCEBwcTFFRESdOnOCBBx5wvEar1bJt2zZef/11jEYjd9999wiBKCkp4eWXX+bKlStj/CGnT59m2bJlXitO5Y0V3vCZZXt7O/v376e8vJzw8HDWr1/P+vXrCQ8Pp6GhgQMHDlBbW8vChQvJy8tjy5YtI8KJoyOC+dcnBGfqu/n9ydZJM/3axUobmUJKSgrr1q3j2rVrSCk5evQoSUlJCCHIyMiYMAjCnaihlk4933k6H22kfyoZeougoCA0Gg0ajQaj0Uh/fz96vZ7+/n76+vocj0NCQgjqNnnlf/r6d6iEQwFAZJh3/BG69lsYDJFeS6UxFVqtlvvvv5/GxkYOHz7syB9VUFBAZWUlHR0dIwo0RUVFsW3bNt544w2MRiPr1q1ziEdwcDAbNmxg3759pKWlOd5DX18fV69e5WMf+5jX+u2tFV7ZsQvUVv2BmpoaMjMz+fznP09hYSHd3d1UVVVx8eJFQkJCyM3N5a677ppyplyYoaUwQ8uzmxdPmOl3NEFBQY5iWAaDgfr6ei5evEhZWRnp6ekIIUhLSxvh83KnSuC5GwN84d/+wBMrraSk2EQrLi5uWiOjrFarY+Vgv9mFYLzHVqvVURY4MjKSiIgIIiMjiYmJYdGiRQTEBvLbiy7YDCfA1/m+VDiuAvBO6vLIEAufv7OZ7o5bxMfHk5KSQnJyMklJSX4REqvVipSS8vJykpKSCA8Px2g0smnTpjFtDQYDb775Jlqtlo0bN45YSRw8eBCr1cqGDRsA+PDDDxkYGODee+/1Wl/Lazp57ieeZ5LN6j/Ax4uTuf/++0lISHCYorq6uhBCkJuby7x586bl4qrX67ly5QpSSjo6OsjOzkYIQcuAhj/94QW3i3K9sEVDhKmZmzdvYrFYSE1NJTk5mdTUVLei90Zjd5qPd+EfvVoYGBggNDTUIQD2+9F/2x9PFZXlrRICrtQ08Xs4rhAiAvgJsBnoBb4qpXxpgrY7gH8CkgAJ/KWUsnzoue8Dnwfso2WWUrqf8EbhMtpImyPSk7QeizU6QgLM3HPPPURFRXHr1i0qKyt5++23SUhIcAjJokWLfCIkAQEB5OTkkJmZyalTp6isrKShoYHly5c7fB12wsLC2Lp1K3v37mXfvn184hOfcNTdXr16Nb/85S9paGhgwYIFnDt3jkceecSrffXWCu/Pv/gFFkUNUFVVxbVr10hLS6OkpITFixdP+wQqMjKSZcuWsWzZMnQ6HZcuXeLgwYMcqI/HYnWvIqQVON+m4YVH78NqtaLT6Rx7SD788ENHVFdqaiopKSlER0djtVoxGo1Trgbsj00m04QCkJiYOOa4N8fZG79Df+T78mjFIYT4NrAE+CMgF9gPbJRSnhrVrgA4DHwCOAF8BvgOkCGl1AkhPgC+KaV8292+qBWH53haL/p//iwXLR1UV1fT3NxMTk4OBQUFaLVampubHT/wlpYWvwhJT08PL730Ek1NTTz++OPk5uaO8VGYzWbeeecdzGYzmzdvdtj9r1y5wtGjRykqKqK2tpatW7d6tW/emFlqQq08vLiG+XFR5Obmkp2d7Xa2X3/RrTfz8Iue7VaP1QTzm78tJATTCD9BX18fLS0t3Lx5k8bGRm7duoXVaiU8PJy4uDgWLlxIbGzsiNWBRqMZ89jV8Flv4++67X6vACiEuAU8JKU8NvT4e4BJSvlXo9ptAVZIKf9x2LF24AHgOKADcqSUje72RQmHd/DWjtXu7m6qq6u5cOECcXFxFBQUsGTJEoKCgjCZTNy6dWtCIUlKSnJqZ7kz6PV6fvjDHzJ//nyCg4NZu3YtKSkpI9pYLBYOHDhAT08PDz74IGFhYbb9DXv3cubMGT7zmc+Qnp7ulf4M5xuvXObNE+7bs+9Kt/KPO3NG+HBmOqfruvmTH5z3+Dz3LawhLcY8oUnIfj8wMGCrFdLURGNjIxqNhpSUFId5a7IIsOlk1u8cF0KEAJpxntIA84HhhlqJTQxGIKXcC+wdds7VQBRwCcgCwoEfCSFWAVeA5+xipPAvX9meRWevyaW0HkWZ0bywfWRKD61Wy+rVq1m1ahW1tbVUV1dz+PBh8vLyyM/Pdzg3wbbr174iOXny5AghSUlJYdGiRW4LSWRkpCP0dtGiRRw4cIB58+axZs0aYmNt1tDAwEDuu+8+Dh06xGuvvcbWrVuJiIggOzubvXv3ulzX2xmuX79OYFslkIY71fcCA+CpLfnEx8+eEqsWi4X2Ltcc4hPxic1bWZPnnGDaK01aLBZaW1u5ceMG586d48CBA8TExDiExF++uOFMtOnxK9uzaGrroabR+Sir8X6HvmLKFYcQ4o+Al8d56j1gAxAmpTQOtX0K+KyUct0k5xPAIeBbUsr/EEKsAF4EngfOAk8A/woIKeW40zEhxNPA08OPRUVFBe3atetOteLwnKmyctoZnWdoKjo6Ojh37hw1NTUsWLCAZcuWkZ6ePsZ8NFxIGhoaaGlpITExkeTkZLeEpKenh927d/P4448TGhrK6dOnOXXqFHl5eZSWljouFlarlaNHj1JfX8/WrVspKyvDYDBgsVh49NFHvWK+0Ov1HDlyhMbGRtatW8ePDhv9NrP0NYODg/T29tLd3U13dzc9PT0j/u7t7UVHAm9eda5k8GR4oy754ODgiJVvc3Mz8fHxjtWIN1e+o5kqVfr6vChOHnmLy5RQcaXfq7/D0fjVVCWEiAfagVgppW7o2LPAJ6SUWyZ4zVrgNeA7UspvTHLuamyO9ted7Y8yVXkfV2L6XcFkMjnKnPb09JCfn09eXt6EO3i9ISQHDx507P0AW4jthx9+SH19PStXriQ/P98hYCdOnODkyZMYjUa++MUv8sYbb5CTk+OoG+JOagyr1cqFCxcoLy8nNzeXlStXotfrKTtyjP8+ZKQL5+tQFGVG+y0L6nDMZjM9PT1jBMF+39fXh0ajQavVEh0dPeLe/nefwer3qCFnMZvNNDc3c+PGDcf3bN68eQ5n+6JFixwBFO7i7KQMrBQmB/CtL66grlnvk9+hnenwcbQAm6WUJ4Yefw8wSin/epy2jwA/BZ6RUv5i2PF7gSVSyh8PO3YJW9TVPmf7ooTDd3TrzU7H9LtKa2sr586d49KlSyQnJ1NQUEBaWtqks/vhQnLz5k1aW1unFBKdTscrr7zCk08+OcIc0drayvvvv8/AwAD33HMPixfbdu2+9NJL1NfX89xzz2GxWPjtb39LwepPUlajdznteXt7O2VlZZjNZtavX09vby/vv/8+Z86cITQ0lLTMbMpb0oZqWEz8vj2dWU6FyWQaIwrDH/f39xMVFTVCCOx/a7VaNBqNU7+9b+7xrCjX1lXzeeFR35tkTCaTIz3KzZs3aW9vZ8GCBQ7T1oIFC1y61riy6dHO8EmCr36H0yEc38Xmo/g0sBR4F9gkpTw5ql0RUA5slVK+O+q5tdj8HxuBSuDPgb8CsqWU/c72RQnH7MZgMCCl5Ny5cxiNRscqZLw65KMxDSXSs69I7KlGhgtJSEgI+/fvJy4ujtLS0hGvt1qt1NXVceTIEeLj4yktLeV3v/sdy5cvp6qqio2feJBv7qnjTANMdWEfnhrDbDZz4sQJzp49y/Lly7FYLBw8eJDm5maioqJISUnBYrGQlpZGbm4uPSTw9ukOn80sjUbjmFWCXRS6u7sxGo2O3E3DBcEuEhqNxis75/0dNeQtDAaDQ0hu3LiBTqdj4cKFjtDf+fPnTzo+/nR4u8J0CIcG+D7wINCPzbz006HnXgDukVLeL4T4f8DjQ22Gs0VKeUgI8XlsPo6FwGngz6SUZ13pixKOuYHVaqW5uZnq6mrq6upIT0+noKCApKQkp30MEwmJVqvlzJkzPPPMM2g0Y+M9zGYzZ86c4c033yQ6OppnnnmGS7XX+fuXr3NrwHkHeVFmNM9tiqKi/H1CQ0MJCQmhsrKSnp4eEhISiIqKIjk5mby8PLKzs8eIo7szS4PBMK4g2I+ZzeYxojBcKDQajd/CUGfqRdQVBgYGaGho4MaNG9y8eZPe3l6SkpIcK5LExETHeM5ksfS7cMwklHDMPQYGBqipqaG6upqAgAAKCgrIzc11OfLFaDQ6TFtvv/02VquVwsLCMSsSsEXe/PjHPyYxMZH29nZO9Rdy6rrrew6SQ1tYGVNDc3MzAwMDJCYmMn/+fJYvX05OTo5TRaOGY09tMVoUhpuTLBbLuIJgv0VERMyYwkU9/WaXqwROl2/HWfR6vcOsdfPmTfr7+x3+kVfPBLP/TLfb5/aleU4JhxKOOYnVaqWhoYHq6mquXbvGkiVLKCgoYMGCBS6fq7W1lddee42NGzdSe62J6tpW2jp7SIzTsmzJPCJCrFy9epWdO3fy/unr/J9f3sSdcFmsFpbo95GZGMDKlStZtmwZqampE5oyrFarow7ERD6GwMBAtFrtCD/DcKEIDw+fMcLgDHZHcdnZDia7Cvnat+Mrent7uXnzJpfrb/LvZWEYBt3vu68CAkBVAFTMUQICAhx7Ovr6+qipqWHfvn2Eh4dTUFBAdna202GT8+bNwxSRyg/ebedsYyi6vgXAAmiGt2qtaI3XyNC0EfbqqxxpSsYt0QAICCTpzq38w+eWOTYU9vX1jSsI9mP2+hB2MYiLi2Px4sWOY/7eY+Br7Bl5f/pqGWdvhXP+VrBPfDvTRVRUFDk5OfSHJmH4g2ebHv2RKt0VlHAoZhUajYaSkhKKi4u5du0a1dXVlJeXk52dTUFBwaQmoI9muHFYGRsO2mcMoI8MbukzMDaFcKHRiNvCAVReM/Lq7/Zh7rcJg70cqv2WkJBARkaGw6w014TBGUwmE8b2S/zzZx/DGhThs+i96WS2lSxwhtn/qShuSwICAj4qCNTTw/nz53njjTeIiYlxpDcZHnPvav2Hk1dNeCIaAH3GQOJT8liZM4/o6OgR9S8UNq5cucLChQsde3hmyozaE+xpTtrb22lra+NMfQ/gull1NL5Ole4KSjgUs57o6GhWrVpFaWkpdXV1jvQmubm55OfnExcX51b9B2/Q22/GbDZjsbif1G8uc/78+UnrvM9kzGYzHR0dDoGw35tMJhISEkhISCAxMZEHFi/h7Wu30OndXzHEaoLJXDh1aLq/UMKhmDMEBgayZMkSlixZQldXF+fOnWPPnj30hy6irNq1KCZv0dJ4jQPNVeh0OoKCgoiJiSEmJgatVktMTAyxsbHExMR4bY/ETGb0jvvECBMdHR1kZmZOd9cmxZ6+fbRAdHd3Exsb6xCIZcuWkZiYSHR09JgghXXLzDM+VborzJyeKBReJDY2lhUrVpCRkcE3fn150qgdn/VBE8zjj9yHNjIYq9WKXq9Hp9PR3d2NTqejsbGRmpoadDodAwMDREdHO4Rl+E2r1fosZ5I/mCgvkybUSsGiPM5d75sxzm+9Xj9GIDo6OggPD3cIRGZmJqWlpcTFxTkdwbmleB5vVbS4vY9jc4nzKWn8gRIOxazGbDaj0+no6uqis7OTrq4ux98mk4nwqDgutKRNS9/yF1mICLFdKQICAhx1p5OSksa0NZlMjgirrq4uR4EinU6HTqcjNDTUsTqxr1bsN39u3HOFqfIy9RkDOHYtiOM/OD9ix70/MJlsq53hAtHe3s7g4KBDIObPn09eXh4JCQkeBy4UZmpZVxDv1qbHewviZ4yw2lHCMYdwJ/nebMBqtdLb20tHR8cIgejs7KSvr4/o6Gji4uKIjY1l/vz5CCGIjY0lKiqKqvoe/ueM5/Uf3Og1aaHN/OxnP6O4uJj8/PxJneMhISEOu/iYMw29f/tKRafTOVKDd3V1YTQaR6xOhguMVqudFqe8q8EIB8920Nlr8voGP4vFgk6nGyMQPT09xMXFkZiYSEJCAkVFRSQmJhIVFeUzEfZWyYKZgNoAOAeYKkXzRMn3ZhL2ndHDRcG+eujq6iI8PJzY2FiHQNjvtVrtiM/bZDLR19dHb28vvb29HLvUzQ/f938Y4x0LTCwLqSQnJ8cRZbNixQry8/M9zrA6GnsOKruoDL/19PQQHh4+rvkrJiaGyMhIn1wo/Z1SxG4KHC0QHR0dREZGjnBWJyQkuGRm8ia+KlngCWrn+G0mHK58Cf1tCpgIo9GITqcbVyCsVusYcYiLiyMmJoaQkBCMRqMjfbddGEbfTCYTGo2G6OhooqKiuNYVzP+eGK8Ome9IjTbwySW30PfY6o8AzJ8/n46ODgYGBsjKymLx4sVEREQQFhZGaGgooaGhjr8nug8NDXX5Im+xWOjr63OY84b7WHQ6HYODg2PMX/ZVS3R0tFsi5+u8TEajkfb29jG+CGCMQHjDzOQLfFWywB3UzvHbiJliChj3/w2ZB0ablbq6uujv7yc2NtYhDCkpKeTn5xMWFsbg4OAIUbh69Srnzp1zPAbbbtzht3nz5pGZmYlGoyEqKoqIiAi6u7u5ePEiJ06c4Ep9A8Hchxn3a3EHWPpJjx3kaneUy6kxLBYLlZWVVFZWsmXLFqKjo6moqODmzZvk5uaSnJyM2WzGaDTS39+PTqfDaDRiMBjG3JvNZkJCQqYUl/Gei46OdlxEh0dvGQyGESuU9vZ26urq6Orqore311Ffwx4BNlxgJkpxsrey1S3RANt39fcnWynM0GKxWOjs7ByzitDr9SPMTBkZGSQkJMxYX894FGZoKczQ8uzmxbNy06NaccxSpju7qD2Fxnji0N3djUajcawW7BewoKAgLBYLer2e3t5e+vr6HCuIkJAQxyrBLgKjb5PNHLu6urhw4QIVFRXcuHGDoKAgsrKyKC4u5ncXwnn3bK/b73XVYgt/+vE4DlVe42idmRv6OPrNH33HIkMsbCpeOOkssa2tjXfffZfIyEg2bNhAX18fx48fp62tjZKSEvLy8qac3VssFkwm07iiYjQanT4WFBTklOCEhIQ4/p/BYECv19Pf3+/4/KxW6xjzV1BYFF/6RSvd/e7vW4kMsfDZvOv093Sg0WgcAmG/j42NnfOhy/5ErThuE87UdXOo2nXRACir7uBMfbfTy2CDwTBGGOz3gYGBI8wtQUFBDrHo7++nra2NGzduEBERMe5Kwf63RqNxy4Hb2dlJdXU1FRUVNDQ0EBwcTF5eHuvXr2fJkiWEhYVRVVVFSEcVAWRhdWsnuJWnttxBXmYseXl5fMFk4uLlq1RUX+dawy3CQkDXUMPn7nmGBQvGr2AIkJiYyI4dOzh58iQvv/wya9as4cEHH+TWrVtUVFRw8uRJSkpKuOOOOyac+AQGBhIWFuaR6cVqtToEZaKVjd2cOJlA2fvT3t5OS0sLZrPZls5eH0l3f+kUvZgcvSmQxXl3cc8y35VuVXiGEo5ZiLdMAXbMZvMIR3RnZyetra20tLSg1+sJCwsjJCSE4OBgx0zParViMpkICwsjKChoXHGIiooiMjLSqyvA9vZ2zpw5w/Hjx2loaCAiIoKCggK2bNlCZmamI6HgpUuXOHr0KPHx8Xy8NJMLum7qe2Jc/n9r82K4MzPW8TgkJISCvKUU5C3FYrHQ3NzMf/93Da+//jphYWFkZGSQmZlJSkrKmPcdFBTEypUryczM5MCBA1y5coX169fz0EMP0dTU5BCQFStWkJeX55OVc0BAgFfEx25eGy0qJ2r7OHbA6fprExIRFaNEYwajhGOW0a03u73asPOH0y1kBpynp/MWLS0t6HQ6goODCQoKcghDeHg4iYmJpKamOqq/2U1J9pvJGkJts96n4b9Wq5X29nYqKys5ceIETU1NREVFsXz5crZv305aWtoIE09TUxNHjhzBaDSSnp7O1atXsVgsvPi5Uv59b6dLoZAZcWb+8bGJzXqBgYEkJSWxZs0akpKSSEpKor6+nmPHjtHZ2Ul6ejoZGRmkp6ePuFDPmzePHTt2cPz4cXbv3s3atWsRQrB161YaGxtHCEhubu6MM70GBAQQEhJCSEjImIJYvUHdcMDz8OeZlJdJMRYlHLOM2mb9iJBbd+g1wIkLN8hNCqOgoMBRHc/uW4iOjp40gudMXTd732/yWfiv1Wqlra2N48ePc/z4cW7dukVCQgLFxcU88cQTJCcnj7Fxd3d3U15eTkNDA8nJybS0tNDW1sZ9991HcnIyAP/51AKnotDASt58I8Xay/TplhIdsXDS/i5atIimpiYKCgpISEigpKSE3t5e6uvruXjxIgcPHmThwoVkZWU5suEGBQVx1113OVYfly9fZv369SQlJfGpT33KISAnTpygtLSUnJycGScg45G1MJIYTbBH39GZlpdJMRblHJ9llNd08txPLnp8nu98IYe7c+Jceo0vw3+tViu3bt3iww8/pLKykpaWFhYtWsSKFSsoKSlhwYIF4wqZwWDg5MmTVFdXs2DBAnp6eggJCeGuu+5i8eLF477mnaOSVw9f47o+bkwoZP4iCykhjXzpc5/k6tWrHDp0iB07dhAVNXHp2La2Nvbu3cuTTz457vNGo5Fr165RV1fH1atX0Wq1ZGVlkZmZSUJCAoODg1RUVHDhwgXH6sNOY2Mjx44dQ6fTUVpaSm5u7ox2DLe2tvLVn5+nstF9U5gvq90pxuJ357gQIgL4CbAZ6MVWc/ylCdp+H/g8OAohmKWUsUPPPQB8G0gB3geelFK6nxFsDhMZ5h1RdNUU4IvwX3tlv6NHj3Lq1Cna29tZvHgxa9eupaSkhPj4+In/h8XCuXPnqKioQKPREBkZSV9fH6tXryYzM3PSsExT+yX+cksGaZk5I0IhowJ7OPju79nx6A5HVFZbWxv79u3j4YcfnjDqKSEhAYPBQF9f37i1zENDQ1m6dClLly5lcHCQxsZG6urqeOuttwAcIpKRkcF7771HbW0t69atIzIykqSkJB5++GEaGho4duzYiBXITBKQxsZGTpw4QUtLCx/LLeR00+CcycukGIunpqoXAS2QDOQC+4UQVVLKU+O0vRP4lJTy7eEHhRALgVeATwHlwHeBHwMPedi3Ocl0mQLcSUt+uq6HF39TOyL812q1cv36dcrLyzl16hQ6nY6srCw2bdpEcXExWu3kJi6r1Vba9YMPPsBoNDpCRletWsXSpUunvJj29fXR0NDAfffdR1hYsKP+g8FgYPfu37Ju3TpiYj5yopeWltLW1sahQ4fYsGHDuIIUEBDAokWLaGxsZOnSpZP+/6CgIFJTU0lNTWXt2rW0tbVRV1fH4cOH6enpRVkB1wAAEjFJREFUIS0tjZ6eHn7+85+zYcMGx/mSk5N55JFHuHnzJseOHeP48eOsXLkSIYRbAuKN9DT2z+LkyZP09vZSXFzM5s2bCQ4OpqrVvXDxmZiXSTEWT4XjMeAhKWUfcFIIsRv4DDBCOIQQAcAy4Mw45/gUcFRK+d5Q2+eBW0KIOCllp4f9m3NoI21+BH+maPY0/LeqTkeUpZXy8nJOnz5NX18fOTk5PPzwwxQVFREREeHUudra2jhy5AgNDQ2OjWz2PRDOXjwvXrxIVlbWCGe11Wrl4MGDLF68mOzs7BHtAwIC2LhxI3v27OHs2bMT1o5ISkpySjhGn3vevHnMmzePlStX0tPTQ11dHbW1teh0On70ox+RnZ3NI4884shhlZKSwrZt28YISHZ2tlNj4I30NBaLhcuXL1NZWYnVaqWkpGSMaM+lvEyKsUx59RBChADj5WzQAPOB4QZ3CTwwTtssIBz4kRBiFXAFeE5KeQzIGX4OKWW7EKILWAocd/J93Fb4O0Wzp+G/X/nu70jpL+eOO+5g586dLFu2zKVw0L6+Po4dO0Z1dbVjs1ppaSl33HGHSykxrFYr58+f5+Mf//iI4+fPn6e9vZ0dO3aM+7rQ0FC2bNnCb37zG+Lj40lNTR3TJikpiffff9/pvoxHdHQ0hYWFFBYWYjAYuHz5Mvv37+dv/uZvKCoqYtWqVWRmZhIfH09KSopjBVJRUUFFRcWkAjKVf6qrz8wbx1p4s6JlQv+U2WympqaGyspKIiMjueuuu0hPTx93FRYdEcx/PpUz4/IyKbyDM5/SI8DL4xx/b+heP+yYHhjPBhKHzXfxT8BZ4Ang98LmBdQAo6fPE50HACHE08DTw49FRUUF7dq1a+J3MYfwZ4pmb4T/DkQu5d++sYPYKNccpiaTiaqqKo4ePYrVanUIRmFhoVsbBpuamgBbFJSdtrY2ysvL2bZt26TnjImJYdOmTbzzzjts3759hDkLPspFZTQavbL/ICwsjPz8fPLz87l+/Tqvvvoqhw4d4uTJk0RGRjoitFJSUkhJSeHGjRtUVFQ4ViDDVwCe+qcMBgPnzp3j9OnTJCYmsnHjRkek2mRERwTzr0+IGZWXSeEdphQOKeUr2HwQIxBCxAPtQARgHDocic1JPvocJ4CNww79WAjxF8A92ERitK1i3PMMO98uYIRK2KOqpng7cwZ/mQK8Ef7bY4CrLQbudFI4rFYrUkrKysrQ6XRERESwatUqioqKPNq4dv78efLy8hwzZJPJxDvvvMPq1avHTWc+mrS0NFasWMHevXt59NFHRwhEcHAw8+fPp7m5mbQ079b/SEtL49lnn+Xo0aNcuXKF3NxcTCYTBw8epL+/n4yMDLKysti6dStNTU0OE1ZpaSlLly512z/19Zcv8alcHWfPniU1NZVPfvKTzJ8/3+X+z/a8TIqxuP2pSSk7hBCtQDZwYuiwwGauGoEQ4l5giZTyx8MOhwEGbGaqzcPaJmJboVx2t2+3A/4yBegN3klJrjc6d56Ghgbee+89rl69SkREBGvWrKG4uNhpP8hEGAwGamtrefzxxx3Hjhw5QmJiInfccYfT5yksLKS1tZUDBw7wwAMPjDDT2P0c3hYOsJnL1q1bx5IlSzhw4AApKSk8+uij9Pf3U1dXR2VlJe+88w6pqank5+cTFBREVVUVbxw8S9mVFLf+56HzXeREG9i+fTtxca6Fbo+HNvKjYATF7MZTuX8F+JoQ4tPYfBKPAZvGaTcI/JcQ4ixQCfw5tlVGGRAP/IsQ4hPAIWyRWr+XUjoVT3w74w9TgL/Cf7u6uigrK+PUqVOEhYWxdu1aVqxYMW54qztcvnyZ5ORkx/kuXbrE9evX+fSnP+1SRtWAgADuvfdefvvb3zpCY+0kJSVx+vRpr/R3IlJSUti5cyfl5eX86le/Yv369Sxfvpzly5fT399PfX09dXV13Lhxg8TEROr1iR6UzQ2gmTSviIZibuGpcDwPfB+oA/qBL0spTwIIIV4A7pFS3i+lPCyE+CtgN7AQOA1sllL2Aw1CiO3At4A04APgsx7267bCl6YAX4f/DgwMUF5eTllZGQEBAaxZs4a7776b6OiJEwa6w4ULFygpKQFAp9Nx6NAhHnroIbdMX8HBwWzevJlf//rXjhrUYPOd/O7371J5uYsBs9VnaVhCQ0O59957ycrK4r333iMtLY01a9YQERFBXl4eeXl5mIaSMf6g0rPtUIeqO3h282JlUlKMQO0cV0zJN/fUehT+O95O4MHBQU6fPs2bb77JwMAAd999Nx/72Md8Mrttb2/n9ddf56mnnsJqtbJnzx6ys7NZvny5R+dtamrirbfeskU36ULYW9nK/hONDAx+dJH1dRVGg8HABx98wPXr19mwYcMIM9npum7+5Aee543a9cwdysQ0h1Fp1RU+wZPwX4Drrf309JuJjgjGarVy5coV9uzZw61bt1i1ahUbN24kMTHRa/0dvbmt9eoFR6qODz74gMjISIqKijz+P4sWLWL5irt59geV1HdHD43PyJ+UM2GunhAWFsaGDRu4evUqf/jDH8jIyGD16tWEhob63T+luH1QwqGYEk/CfwFO1fbw5Zcu8jdb4tj35mvU1NRQVFTEH//xH7NgwQKv9XOizW1hgVY2FMbRE3iJBil57LHHvFIprqffzK6jUKub2qzm6yqM6enp7Ny5k8OHD/OrX/2KjRs3EhnmnVWCylSrGI0SDoVTuBP+O5zTdT188VvV3J9u5vnnnx93E527TLW5zWAJYd9pHftOW7lraTHbcX0PyHh4Kw2LtwgLC2Pjxo3U19ezf/9+ktKyiImMRKd3f8WgMtUqxmPmZElTzGjs4b/LM913WuuC07n3oae8Lhp//dJFDp6dKlU6QAAfXu7nyy9dpKffs70p3qjC6CsyMjLYuXMngRYDyWHtHp3L1fQ0itsDJRwKp4mOCCZ1vvv7KazAvso273UIz2b9nuCNKoy+JDw8nE2bNrF9XQa4GZCrMtUqJkIJh8JpvJF+5FB1B916z2b7dqZr1j/TxmEyHliTw8fuiJ264TioTLWKiVDCoXAab6Qf6eozU9esn7qhE0zXrH+mjcNUfPXT2RS5aGIsyozmL7Ys5lStjvKaTk7V6vwidIrZgTJeKpxmJoV3emvW787mtpk0Ds4wPD1NWXUHk2/dslKUEcXC+DA++3+rfVIaWDH7UcKhcJrpqj44Ht6c9bu6uW0mjYOzjE5P815VCz0DHz0fqwlmdW4cNfXNVNX3Yq3vG3MOX+9JUcwe1KeucJrpqj44HtM5659J4+Aqw9PTnL/aTsXx0xgHern/3pV87w866tqnFjNf70lRzHyUj0PhNPbqg57grfDO6Zz1z6RxcBdtZDB35S3gS09s4qF7C/j67nPTEp2mmJ0o4VC4xJbieQS6uenam+Gd9lm/J3gy658p4+ApAQEBDIQs4lqfe5FXvt6TopiZKOFQuIQ9/Yg7eDO8c7pn/TNlHLzB3srWKRzmE+OPPSmKmYcSDoXLfGV7llvhna5WH5yK6Z71z5Rx8ITZtCdFMXNQwqFwGXt45/pl8VNeuAMDYMOyeJ84Uad71j9TxsETZtueFMXMYOZ8gxWzCn9UH3QGf9Ven4iZMg7uMtv2pChmBko4FB7hy+qDzuCv2utTMd3j4C6zcU+KYvqZud9oxaxCGxk8bVXiZtKsfzrHwR1m854UxfThkXAIISKAnwCbgV7gq1LKl8Zp9wLwwrBDAUAksFNKuVsI8WXgRcA4rE22lLLRk/4pbi9m66x/OrFHp3lSGni696Qo/I+nn/aLgBZIBnKB/UKIKinlqeGNpJQvDrUFQAjxD8BGYM/QoTuBL0kp/9vD/igUs27WP914Uhp4Ju1JUfgPT6OqHgO+KaXsk1KeBHYDn5nsBUKIZcBfA49LKU1Dh+8EznjYF4VC4QbTHZ2mmH1MueIQQoQAmnGe0gDzgYvDjknggSlO+Z/Af0gprw2dPxwQwN8JIVYBTcALUsrfT919hULhDaY7Ok0xu3DGVPUI8PI4x98buh8ewK3H5rsYFyFEEVAydE4784Fy4HvANuA+4NdCiBIp5cWxZwEhxNPA08OPRUVFBe3atWvyd6JQKMZlpkSnKWYHU37qUspXgFdGHxdCxAPtQAQfObUjsTnJJ+JJYLeU0jGtkVJeB9YNa7NXCFEG3M/I1czwPu0CRqhEZWVlDNA1+btRKBQTMZOi0xQzG7enC1LKDiFEK5ANnBg6LLCZqybiQWzi4UAIcSdwn5Ty34cdDgMM7vZNoVC4j4pOU0yFp9+CV4CvCSE+DSzF5izfNF5DIcQ8IA04Puqp3qFz1AC/x2bGWgU84WHfFAqFB6joNMVEeBpV9TzQAtQBbwBfHoquQgjxghDi7WFt04EuKeXA8BNIKa8AfwT8K9ADfAX4pJSyycO+KRQKhcIHBFjdzac8w7D7OO68806CglT6A4VCoXCGwcFBqqqqAGKLi4t1zrxGZcdVKBQKhUvMOU/X4KDK0qlQKBTO4s41cy4Jhxagurp6uvuhUCgUsxEt4JSpai4Jx01sUVteKYD89NNPH9q1a9c6b5xrtqLGQI0BqDGwM8fHQYvtGuoUc0Y4iouLrcANb52vt7d30FlH0VxFjYEaA1BjYGeOj4NL70s5xxUKhULhEko4FAqFQuESSjgUCoVC4RJKOCZGpdpVYwBqDECNgR01DkPMmZ3jCoVCofAPasWhUCgUCpdQwqFQKBQKl1DCoVAoFAqXUMKhUCgUCpeYMzvHPUEIEQH8BNiMrbDUV6WUL03xmg3AASBUSmmerO1swJUxEELsAP4JSMJW8fEvpZTl/uqrtxFCrAT+B1s1yyrgSSnl5VFtAoFvAY8DFuDbUsoX/d1XX+HkGEQA3wa2Yrt27AP+Qko5J0o2OzMGo9r/AgiQUn7GT12cMagVh40XseVqScb2o/gPIcTyiRoLIbTYLrIB/umeX3BqDIQQBdh+XE8AscD3gLeEEDF+7KvXEEKEA69jE4VYYD+wZ5ymfw6sxlYeeRXwBSHEp/zVT1/iwhh8DcgAcoFMIAb4L//00re4MAb29luxVTy9LVHCYeMx4JtSyr6hCoa7gclmEd8GXvNLz/yHs2OwGPiulLJCSmmRUv4csAI5fuyrN7kX6JZS/kJKaQK+CSwWQiwb1e4x4L+klG1Sylrg+9hWH3MBZ8cgHPi6lLJTStmNbfJ0t5/76iucHQOEEInAvwE/9XMfZwy3jalKCBECaMZ5SgPMBy4OOyaBByY4zwPAEuBJ4K+820vf4o0xkFLuBfYOO+dqIAq45NXO+o8chr1vKeWgEKJ26PjZidphG58/9ksPfY9TYyCl/NKo130SmCt1DJz9HgD8EPh3IBXbteC243ZacTwCdI5z+9nQ8/phbfVA5OgTCCHigO8AT2GbZc82PB6D4QghBPAq8PdSyk6v99Y/aBj5vmH89z663ZTjM4twdgwcCCG+hO379Pc+7Jc/cWoMhvx70VLK//VXx2Yit82KQ0r5CvDK6ONCiHigHYgAjEOHI7E5iEfzPeD7UspaIUS6j7rqM7w0BvbXrMVmrvuOlPI/vN9bv6HH9r6HM957H91u0vGZZTg7BgghArCZaT4LfFxKOVtXmqOZcgyEEAuw+QLX+a9bM5PbacUxLlLKDqAVWySFHYHNFDGaR4B/FkJ08dHytU0Isca3vfQtLo4BQohHsJmrnpNSfsP3PfQpFxn2voUQQdjMD6Pf+4h2TDI+sxCnxkAIEQy8jC3ybpWU8pQ/O+ljnBmDjcBCoHroGvB3wHYhxGhT1pzntllxTMErwNeEEJ8GlmJzhG4a3UhK6ZiRDK046oHEuRCOi5NjIIQoAn4BbJVSvuvfLvqEMiBBCPE54FfYLgbXgHOj2r0C/K0Q4jA2s8azwF/7s6M+xNkx+BcgD1g9V0JwhzHlGEgpfwn80v5YCPE1YIkKx719eR5oAeqAN4AvD0UWIYR4QQjx9nR2zk84OwZfAsKA14QQvcNu66aj054ipezHNoP+M2zmuvuAbVJKqxDivBBi51DT72K7uFQBx4BdUso5EVnnzBgIIUKBv8C20ro57HOfE6suF74HClR2XIVCoVC4iFpxKBQKhcIllHAoFAqFwiWUcCgUCoXCJZRwKBQKhcIllHAoFAqFwiWUcCgUCoXCJZRwKBQKhcIllHAoFAqFwiWUcCgUCoXCJf4/mQCdM0Ff8oEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st community\n",
    "nx.draw_networkx_nodes(karate, layout, nodelist=comm[0][0],\n",
    "                       node_color='r')\n",
    "# 2nd community\n",
    "nx.draw_networkx_nodes(karate, layout, nodelist=comm[0][1],\n",
    "                       node_color='b')\n",
    "# draw edges\n",
    "nx.draw_networkx_edges(karate, layout, width=1.0, alpha=0.5)"
   ]
  }
 ],
 "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": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "813px",
    "left": "0px",
    "right": "1468px",
    "top": "135px",
    "width": "212px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}