Graph_Experiments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing Jupyter notebook from Preprocessing.ipynb\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os, sys, datetime\n",
    "import pandas as pd\n",
    "import nbimporter\n",
    "\n",
    "import Preprocessing as pre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Complete Social + Action Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563069\n",
      "304691\n",
      "466545\n",
      "456626\n",
      "14855842\n"
     ]
    }
   ],
   "source": [
    "# Load weighted digraph from file \n",
    "\n",
    "G = nx.read_weighted_edgelist(\"Data/higggs-complete-graph.edgelist\",\n",
    "                              create_using=nx.DiGraph(), nodetype=int)\n",
    "\n",
    "social = pre.load_social_graph()\n",
    "\n",
    "log = pre.read_log()\n",
    "\n",
    "print(log.shape[0])\n",
    "print(len(G))\n",
    "print(len(G.edges))\n",
    "print(len(social))\n",
    "print(len(social.edges))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Degree Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_degree = sorted([d for (n, d) in social.in_degree()], reverse=True)\n",
    "out_degree = sorted([d for (n, d) in social.out_degree()], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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82dC3mfUBern7RYmfzwN6AP8GzgemAx+4+4jq9puuobKyWZKbNm2ifv36td6f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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4110426d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.semilogy(range(len(social)), in_degree, color=\"k\")\n",
    "plt.title(\"In-degree dist.\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.semilogy(range(len(social)), out_degree, color=\"k\")\n",
    "plt.title(\"Out-degree dist.\")\n",
    "\n",
    "for ax in fig.axes:\n",
    "    plt.sca(ax)\n",
    "    plt.xticks(rotation=310)\n",
    "\n",
    "plt.savefig(\"Report/Figures/deg.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13408\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'generator' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-84c64867cb1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber_connected_components\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnected_components\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_undirected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'generator' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "print(nx.number_connected_components(G.to_undirected()))\n",
    "C = nx.connected_components(G.to_undirected())\n",
    "print([len(c) for c in C[0:30]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Action Log Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350423\n",
      "212646\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Compute \"Friendliness\" of events\n",
    "\"\"\"\n",
    "\n",
    "activities_between_friends = 0\n",
    "activities_between_strangers = 0\n",
    "\n",
    "for date, action in log.iterrows():\n",
    "    if (social.has_edge(action[\"user1\"], action[\"user2\"]) or social.has_edge(action[\"user2\"], action[\"user1\"])):\n",
    "        activities_between_friends += 1\n",
    "    else:\n",
    "        activities_between_strangers += 1\n",
    "        \n",
    "print(activities_between_friends)\n",
    "print(activities_between_strangers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user 1 had influence 0 times, has 16280 followers, and follows 22 users\n",
      "user 2 had influence 3 times, has 4707 followers, and follows 77 users\n",
      "user 3 had influence 0 times, has 137 followers, and follows 25 users\n",
      "user 4 had influence 195 times, has 8643 followers, and follows 402 users\n",
      "user 5 had influence 29 times, has 2194 followers, and follows 58 users\n",
      "user 6 had influence 186 times, has 27088 followers, and follows 101 users\n",
      "user 7 had influence 39 times, has 2146 followers, and follows 99 users\n",
      "user 8 had influence 1280 times, has 32106 followers, and follows 54 users\n",
      "user 9 had influence 0 times, has 567 followers, and follows 29 users\n",
      "user 10 had influence 1 times, has 10204 followers, and follows 305 users\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "for u in social:\n",
    "    if i < 10:\n",
    "        print(\"user {} had influence {} times, has {} followers, and follows {} users\"\n",
    "              .format(u, pre.num_times_spread(log, u), pre.num_followers(social, u), pre.num_following(social, u)))\n",
    "    i += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'networkx.classes.multidigraph.MultiDiGraph'>\n",
      "16.0\n",
      "32.5339380587\n",
      "4.0\n",
      "32.5339380587\n"
     ]
    }
   ],
   "source": [
    "social = social.to_directed()\n",
    "\n",
    "print(type(social))\n",
    "print(np.median([d for (_, d) in social.out_degree()]))\n",
    "print(np.mean([d for (_, d) in social.out_degree()]))\n",
    "\n",
    "print(np.median([d for (_, d) in social.in_degree()]))\n",
    "print(np.mean([d for (_, d) in social.in_degree()]))\n",
    "\n",
    "# Interesting! (32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write heuristic seed sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"Why we need to subgraph:\"\"\"\n",
    "\n",
    "soc_nodes = np.array(social.nodes)\n",
    "G_nodes = np.array(G.nodes)\n",
    "\n",
    "print(len(soc_nodes))\n",
    "print(len(G_nodes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Write top-k influencial + indeg + rand users to file\n",
    "k = 50\n",
    "\n",
    "_infl = pre.get_top_k_infl(log, k)\n",
    "_in = pre.get_top_k_indeg(social.subgraph(G_nodes), k)\n",
    "_deg = pre.get_top_k_deg(social.subgraph(G_nodes), k)\n",
    "_rand = []\n",
    "\n",
    "for i in range(1, k+1):\n",
    "    _rand.append(np.random.choice(G_nodes))\n",
    "\n",
    "np.save(\"Data/seeds/infl/infl{}.npy\".format(k), _infl)\n",
    "np.save(\"Data/seeds/in_deg/in_deg{}.npy\".format(k), _in)\n",
    "np.save(\"Data/seeds/deg/deg{}.npy\".format(k), _deg)\n",
    "np.save(\"Data/seeds/rand/rand{}.npy\".format(k), _rand)\n",
    "    \n",
    "print(_infl[0:10])\n",
    "print([pre.num_times_spread(log, u) for u in _infl[0:10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=1\n",
    "#from spread import *\n",
    "\n",
    "#num_seeds = list(range(1, 50))\n",
    "#indeg_spreads = []\n",
    "#deg_spreads = []\n",
    "#infl_spreads = []\n",
    "#rand_spreads = []\n",
    "#\n",
    "#for i in num_seeds:\n",
    "#    print(\"Computing spreads for {} seeds\".format(i))\n",
    "#    indeg_seeds = np.load(\"Data/seeds/in_deg/in_deg{}.npy\".format(i))\n",
    "#    #deg_seeds = np.load(\"Data/seeds/deg/deg{}.npy\".format(i))\n",
    "#    #infl_seeds = np.load(\"Data/seeds/infl/infl{}.npy\".format(i))\n",
    "#    rand_seeds = np.load(\"Data/seeds/rand/rand{}.npy\".format(i))\n",
    "#    \n",
    "#    (indeg_n, _) = get_expected_spread(G, indeg_seeds, 100, mean=True)\n",
    "#    #(deg_n, _) = get_expected_spread(G, deg_seeds, 100, mean=True)\n",
    "#    (rand_n, _) = get_expected_spread(G, rand_seeds, 100, mean=True)\n",
    "#    #(infl_n, _) = get_expected_spread(G, infl_seeds, 100, mean=True)\n",
    "#    \n",
    "#    indeg_spreads.append(indeg_n)\n",
    "#    #deg_spreads.append(deg_n)\n",
    "#    rand_spreads.append(rand_n)\n",
    "#    #infl_spreads.append(infl_n)\n",
    "#\n",
    "#plt.plot(num_seeds, indeg_spreads, c='k')\n",
    "##plt.plot(num_seeds, deg_spreads, c='b')\n",
    "#plt.plot(num_seeds, rand_spreads, c='y')\n",
    "##plt.plot(num_seeds, infl_spreads, c='r')\n",
    "#\n",
    "#plt.show()\n",
    "#\n",
    "#plt.plot(num_seeds, indeg_spreads, c='k')\n",
    "#plt.plot(num_seeds, deg_spreads, c='b')\n",
    "#plt.plot(num_seed, infl_spreads, c='r')\n",
    "#plt.plot(num_seeds, rand_spreads, c='y')\n",
    "#\n",
    "#plt.show()\n",
    "#\n",
    "#np.save(\"Data/spreads/rand_spreads\", indeg_spreads)\n",
    "#np.save(\"Data/spreads/indeg_spreads\", indeg_spreads)\n",
    "#np.save(\"Data/spreads/deg_spreads\", deg_spreads)\n",
    "#np.save(\"Data/spreads/infl_spreads\", infl_spreads)\n"
   ]
  }
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