rhiever/reddit interest correlations.ipynb

## reddit interest correlations.ipynb
{
 "metadata": {
  "name": "reddit interest correlations"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# What to show in paper\n",
      "\n",
      "(1) <del>In the methods/materials section, describe the basic properties of the raw user-by-subreddit data (number of users, number of subreddits, mean & max subreddits per user, mean & max users per subreddit).</del>\n",
      "\n",
      ">number of users:  876961\n",
      "\n",
      ">number of subreddits:  15122\n",
      "\n",
      ">avg number of subreddits per user:  9.6875425475\n",
      "\n",
      ">min number of subreddits per user:  1\n",
      "\n",
      ">max number of subreddits per user:  112\n",
      "\n",
      ">avg number of users per subreddit:  561.803795794\n",
      "\n",
      ">min number of users per subreddit:  1\n",
      "\n",
      ">max number of users per subreddit:  523025\n",
      "\n",
      "(2) <del>Also in methods/materials section, describe the basic properties of the weighted, unpruned network (e.g. mean & max edge weight)</del>\n",
      "\n",
      ">mean subreddit-subreddit edge weight (raw count):  17.7689720079\n",
      "\n",
      ">min subreddit-subreddit edge weight (raw count):  1.0\n",
      "\n",
      ">max subreddit-subreddit edge weight (raw count):  309985.0\n",
      "\n",
      ">mean subreddit-subreddit edge weight (serrano normalized):  0.00245071366697\n",
      "\n",
      ">min subreddit-subreddit edge weight (serrano normalized):  3.20039121582e-07\n",
      "\n",
      ">max subreddit-subreddit edge weight (serrano normalized):  0.818181818182\n",
      "\n",
      "(3) For the \"both nodes\" and \"either nodes\" versions of the Serrano-pruned network, present the following descriptive statistics over a range of alphas:\n",
      "\n",
      "(3a) <del>Clustering coefficient</del>\n",
      "\n",
      "(3b) <del>Characteristic path length (aka mean geodesic distance)</del>\n",
      "\n",
      "(3c) Exponent fitting the degree distribution to a power law (this can be tricky; we can revisit it later)\n",
      "\n",
      "(3d) <del>Number of nodes with degree > 0 (maybe as a fraction of the number seen when alpha = 1)</del>\n",
      "\n",
      "(3e) <del>Number of edges (again, maybe as a fraction of the number when alpha = 1)</del>\n",
      "\n",
      "(3f) <del>Modularity and number of communities</del>\n",
      "\n",
      "Note that all the descriptive statistics 3a-3e are topological properties; they're computed for the pruned, unweighted network.  I think we'll only be using the weights in the pruned network for community detection (3f) and viz layout.  For the presentation of 3, we might try a six-panel figure; one panel for each statistic, and each panel containing one line for the \"both nodes\" network and one line for the \"either nodes\" network.\n",
      "\n",
      "For the viz, I agree, we can just show the Serrano \"both nodes\" network.  If this gives a nice layout and clustering, we may be able to skip the aggregation-into-clusters suggestion I made earlier."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Serrano method: sweeping alpha on logarithmic scale\n",
      "\n",
      "## Either edge significant"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as pyplot\n",
      "from pandas import *\n",
      "\n",
      "eithersig = read_csv(\"reddit-interest-correlations/reddit-interest-cooccurrence-all-backbone-network-pval1.0-network-stats.csv\")\n",
      "\n",
      "fig = pyplot.figure(figsize=(12, 10))\n",
      "ax = fig.add_subplot(3,2,1)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"num_nodes\"], \"ko\")\n",
      "ylabel(\"# of nodes (thousands)\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "yticks(arange(3000, 12001, 1000), arange(3, 12.1, 1))\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,2)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"num_edges\"], \"ko\")\n",
      "ylabel(\"# of edges (thousands)\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "yticks(arange(0, 2500001, 500000), arange(0, 2501, 500))\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,3)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"avg_clustering_coeff\"], \"ko\")\n",
      "ylabel(\"Avg. clustering coefficient\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,4)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"avg_shortest_path_length\"], \"ko\")\n",
      "ylabel(\"Avg. shortest path length\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,5)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"num_communities\"], \"ko\")\n",
      "ylabel(\"# of communities\", fontsize=12)\n",
      "xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,6)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(eithersig[\"alpha\"], eithersig[\"modularity_value\"], \"ko\")\n",
      "ylabel(\"Modularity\", fontsize=12)\n",
      "xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "print \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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sXbpUY462pXxeIiJdiM1hogvt9PR0vPHGG+jbt6/Glr7/+7//q1uU/+eLL77A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\nRERERDJgok1EREREJIP/AzomJbOnWXYaAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Both edges significant"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as pyplot\n",
      "from pandas import *\n",
      "import networkx as nx\n",
      "\n",
      "bothsig = read_csv(\"reddit-interest-correlations/reddit-interest-cooccurrence-all-backbone-network-pval1.0-bothsig-network-stats.csv\")\n",
      "bothsig[\"rand_avg_shortest_path_length\"] = bothsig[\"rand_avg_shortest_path_length\"].replace(0.0, float(\"nan\"))\n",
      "bothsig[\"rand_avg_clustering_coeff\"] = bothsig[\"rand_avg_clustering_coeff\"].replace(0.0, float(\"nan\"))\n",
      "\n",
      "fig = pyplot.figure(figsize=(12, 10))\n",
      "ax = fig.add_subplot(3,2,1)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"num_nodes\"], \"ko\")\n",
      "ylabel(\"# of nodes (thousands)\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "yticks(arange(0, 12001, 2000), arange(0, 12.1, 2))\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,2)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"num_edges\"], \"ko\")\n",
      "ylabel(\"# of edges (thousands)\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "ylim(ymin=-50000)\n",
      "yticks(arange(0, 2500001, 500000), arange(0, 2501, 500))\n",
      "\n",
      "# generate random graphs to compare clustering coefficient & shortest path length to\n",
      "\"\"\"rand_avg_clustering_coeff = []\n",
      "rand_avg_shortest_path_length = []\n",
      "\n",
      "for i in range(len(bothsig[\"alpha\"])):\n",
      "    if bothsig[\"alpha\"][i] == 0.05:\n",
      "        avg_clustering_coeff = 0.0\n",
      "        avg_shortest_path_length = 0.0\n",
      "        \n",
      "        for i in range(20):\n",
      "            cur_graph = nx.gnm_random_graph(bothsig[\"num_nodes\"][i], bothsig[\"num_edges\"][i])\n",
      "            \n",
      "            while not nx.is_connected(cur_graph):\n",
      "                cur_graph = nx.gnm_random_graph(bothsig[\"num_nodes\"][i], bothsig[\"num_edges\"][i])\n",
      "            \n",
      "            avg_clustering_coeff += nx.average_clustering(cur_graph)\n",
      "            avg_shortest_path_length += nx.average_shortest_path_length(cur_graph)\n",
      "            \n",
      "        avg_clustering_coeff /= 30.0\n",
      "        avg_shortest_path_length /= 30.0\n",
      "            \n",
      "        rand_avg_clustering_coeff.append(avg_clustering_coeff)\n",
      "        rand_avg_shortest_path_length.append(avg_shortest_path_length)\n",
      "    else:\n",
      "        rand_avg_clustering_coeff.append(0.0)\n",
      "        rand_avg_shortest_path_length.append(0.0)\n",
      "\n",
      "bothsig[\"rand_avg_clustering_coeff\"] = rand_avg_clustering_coeff\n",
      "bothsig[\"rand_avg_shortest_path_length\"] = rand_avg_shortest_path_length\"\"\"\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,3)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"avg_clustering_coeff\"], \"ko\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"rand_avg_clustering_coeff\"], \"o\")\n",
      "ylabel(\"Avg clustering coefficient\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,4)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"avg_shortest_path_length\"], \"ko\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"rand_avg_shortest_path_length\"], \"o\")\n",
      "ylabel(\"Avg shortest path length\", fontsize=12)\n",
      "#xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,5)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"num_communities\"], \"ko\")\n",
      "ylabel(\"# of communities\", fontsize=12)\n",
      "xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "#fig = pyplot.figure(figsize=(15, 7))\n",
      "ax = fig.add_subplot(3,2,6)\n",
      "ax.set_xscale(\"log\")\n",
      "plot(bothsig[\"alpha\"], bothsig[\"modularity_value\"], \"ko\")\n",
      "ylabel(\"Modularity\", fontsize=12)\n",
      "xlabel(r'$\\alpha$', fontsize=12)\n",
      "xlim(0.91e-4, 1.2)\n",
      "\n",
      "print \"Blue is an average from 20 random networks of same size\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Blue is an average from 20 random networks of same size\n"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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ZhqjuEH0r4MyfP1/SyhARkWEUCoXO8ilTppi5JkREVK3OebT37NkDQRAwdOhQ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\n"
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Power law fitting for the distribution of node degrees"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#import powerlaw\n",
      "import plfit\n",
      "data = []\n",
      "\n",
      "with open(\"reddit-interest-correlations/reddit-interest-serrano-degree-dist-pval1.0.csv\") as infile:\n",
      "    for degree in infile.readlines()[1].split(\",\"):\n",
      "        data.append(int(degree))\n",
      "\n",
      "\n",
      "#myplfit = plfit.plfit(data)\n",
      "#pval = myplfit.test_pl(print_timing=True)\n",
      "\n",
      "#print pval\n",
      "\n",
      "\"\"\"results = powerlaw.Fit(data, discrete=True)\n",
      "print \"\"\n",
      "R, p = results.distribution_compare('power_law', 'lognormal')\n",
      "print \"power law vs. lognormal\"\n",
      "print \"R = \", R\n",
      "print \"p = \", p\n",
      "print \"\"\n",
      "R, p = results.distribution_compare('power_law', 'exponential')\n",
      "print \"power law vs. exponential\"\n",
      "print \"R = \", R\n",
      "print \"p = \", p\n",
      "print \"\" \"\"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "'results = powerlaw.Fit(data, discrete=True)\\nprint \"\"\\nR, p = results.distribution_compare(\\'power_law\\', \\'lognormal\\')\\nprint \"power law vs. lognormal\"\\nprint \"R = \", R\\nprint \"p = \", p\\nprint \"\"\\nR, p = results.distribution_compare(\\'power_law\\', \\'exponential\\')\\nprint \"power law vs. exponential\"\\nprint \"R = \", R\\nprint \"p = \", p\\nprint \"\" '"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}