Demo analysis of KONECT's Route Views network dataset ... (dataset from January 02 2000)

notebook2-as20000102.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:2db651babf99a668d53ec70b70deeb42ca94b1112641764544957c1e8fa2060b"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from scipy import stats, integrate\n",
      "import matplotlib.pyplot as plt\n",
      "from operator import mul\n",
      "import requests\n",
      "import re\n",
      "\n",
      "from graph_tool.all import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Load Dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "NETWORK_NAME = 'as20000102' # arenas-pgp, topology, as20000102"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "g = graph_tool.collection.konect_data[NETWORK_NAME] "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = requests.get('http://konect.uni-koblenz.de/networks/' + NETWORK_NAME).text\n",
      "print re.search('description\">(.*)</div></div>', r).group(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "This is the undirected network of autonomous systems of the Internet connected with each other. Nodes are autonomous systems (AS), and edges denote communitation. The network contains loops.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Helper Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_hist(data, xlog=False, ylog=True, label=''):\n",
      "    plt.bar(range(len(data[0])), data[0], log=ylog)\n",
      "    if xlog:\n",
      "        plt.xscale('log')\n",
      "    plt.title(label + ' Histogram')\n",
      "    plt.xlabel(label)\n",
      "    plt.ylabel('Frequency')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def avg_from_hist(data):\n",
      "    ammount = data[0]\n",
      "    label = data[1][:-1]\n",
      "    return sum(map(mul, ammount, label)) / sum(ammount)\n",
      "\n",
      "# for degrees, this is equal to vertex_average(g, 'total')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Shortest-distance\n",
      "the shortest-distance for each vertex pair in the graph."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "distance_hist = distance_histogram(g)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_hist(distance_hist, label='Shortest Distance')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEZCAYAAACNebLAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w+UmT9YG9nLNgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f643dac21d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Average Shortest-distance"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg_from_hist(distance_hist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "3.7050034741874245"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Degree\n",
      "the vertex degree."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "degree_hist = vertex_hist(g, \"total\")\n",
      "# degree_hist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_hist(degree_hist, label='Degree')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEZCAYAAACAZ8KHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w8lhdFCfPmVIwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f641d56bc10>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_hist(degree_hist, xlog=True, label='Degree')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEfCAYAAABSy/GnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSJI6+f+thTWgIqm4RQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f641d373810>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Average Degree"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "avg_from_hist(degree_hist)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "4.2925548347235098"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vertex_average(g, 'total')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "(4.29255483472351, 0.31167935697421306)"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}