cascade-stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import networkx as nx\n",
    "import pickle as pkl\n",
    "import pandas  as pd\n",
    "from glob import glob\n",
    "from graph_tool import load_graph, GraphView\n",
    "from graph_tool.all import global_clustering\n",
    "from helpers import cascade_source, infected_nodes\n",
    "\n",
    "from collections import Counter\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_for_graph(graph, stat_extractor):\n",
    "    dataset_id = '{}-msi-s0.1-o0.1-omuniform/'.format(graph)\n",
    "    cascade_path_patterns = os.path.join('cascade-with-edges/', dataset_id, '*.pkl')    \n",
    "    \n",
    "    cascades = [pkl.load(open(p, 'rb')) for p in glob(cascade_path_patterns)]\n",
    "    \n",
    "    depths = []\n",
    "    for _, c, edges in tqdm(cascades):\n",
    "        source = cascade_source(c)\n",
    "\n",
    "        g = nx.Graph()\n",
    "        g.add_edges_from(edges)\n",
    "        depths += stat_extractor(g, source)    \n",
    "    df = pd.DataFrame(depths)\n",
    "    df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_depths(g, source):\n",
    "    return list(nx.shortest_path_length(g, source=source).values())    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_degs(g, source):\n",
    "    return list(nx.degree(g).values())    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wiener_index(g, *args):\n",
    "    return [nx.algorithms.wiener.wiener_index(g)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_tree_depth(g, source):\n",
    "    return [max(extract_depths(g, source))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ovZeLiEiHaaBXyunVUu4ANaXcAVok5Q5QSynfQ6XkHIYGuohIS6hDb1CMzjZCBojQ2cY4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\npb9XeOR/5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbf0e1358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('grqc', extract_tree_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rXUBRrWfMrfc3Cwe6JDXCDL2IMebYNdc2Q1d7zNAlacQc6L11tQsorKtdQEFd7QKKaj1j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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbb541e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('lattice-1024', extract_tree_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wty16qPdNN/Jb6OMQaUo3emI/uqaxTD0iPgT8C3ByRHw3Il4HvAt4YUTcApwx3pYktWzd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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbba37b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('fb-messages', extract_tree_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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denq2ZuwAM9UzyzrUM89m7AC91DPP+bjUJWmF2KkvURm9pZ1lp4zzgBLOxFmUx05dkuRS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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbba1e748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('infectious', extract_tree_depth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbe1e0f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('lattice-1024', wiener_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HFTi2Xylz76Xk7IMKgYjbQ0R8sQsii4+MgaaGZqToAE4pOoBTig7gkKIDOKXoAC6lzL2X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\nQiAiMnIqBCIiI6dCICIycioEIiIj9+8lRCUgI9VY/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbb307550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('grqc', wiener_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3bbe187710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_for_graph('infectious', wiener_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stats_for_graph(graph, func):\n",
    "    g = load_graph('data/{}/graph.gt'.format(graph))\n",
    "    \n",
    "    dataset_id = '{}-msi-s0.1-o0.1-omuniform/'.format(graph)\n",
    "    cascade_path_patterns = os.path.join('cascade/', dataset_id, '*.pkl')    \n",
    "    \n",
    "    cascades = [pkl.load(open(p, 'rb')) for p in glob(cascade_path_patterns)]\n",
    "    \n",
    "    stats = []\n",
    "    for _, c in cascades:\n",
    "        inf_nodes = infected_nodes(c)\n",
    "        vfilt = g.new_vertex_property('bool')\n",
    "        vfilt.a = False\n",
    "        vfilt.a[inf_nodes] = True\n",
    "        subg = GraphView(g, vfilt=vfilt)\n",
    "        stats.append(func(subg))\n",
    "    df = pd.Series(stats)\n",
    "    return df.describe()\n",
    "    # df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphs = ['lattice-1024', 'infectious', 'fb-messages', 'email-univ', 'grqc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lattice-1024 0.0\n",
      "infectious 0.5303511944801272\n",
      "fb-messages 0.08739802560816524\n",
      "email-univ 0.2618482750951399\n",
      "grqc 0.7350589235172901\n"
     ]
    }
   ],
   "source": [
    "for graph in graphs:\n",
    "    print(graph, stats_for_graph(graph, lambda g: global_clustering(g)[0])['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lattice-1024 0.03318865410241766\n",
      "infectious 0.22103658536585372\n",
      "fb-messages 0.054471941007374076\n",
      "email-univ 0.05891942244993013\n",
      "grqc 0.023628938832252085\n"
     ]
    }
   ],
   "source": [
    "def density(g):\n",
    "    return 2 * g.num_edges() / g.num_vertices() / (g.num_vertices() - 1)\n",
    "\n",
    "for graph in graphs:\n",
    "    print(graph, stats_for_graph(graph, density)['mean'])"
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}