DBCerigo/DeltaCon_v1.ipynb

## DeltaCon_v1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "import networkx as nx\n",
    "import scipy.sparse as sp\n",
    "import numpy as np\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [DeltaCon](http://arxiv.org/pdf/1304.4657v1.pdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compute_FBP_invs(A):\n",
    "\n",
    "    # max power to calculate inverse to\n",
    "    max_pw = 7\n",
    "\n",
    "    # make identity\n",
    "    I = sp.identity(A.shape[0])\n",
    " \n",
    "    # remove self links - can't propogate beliefs to self - need to consider if the self links should be counted in\n",
    "    # degree still?\n",
    "    #A = A - sp.dia_matrix((A.diagonal(), [0]), shape=(A.shape[0], A.shape[0]))\n",
    "\n",
    "    # make degree matrix\n",
    "    ds = [int(x) for x in A.sum(axis=1)]\n",
    "    D = sp.dia_matrix((ds, [0]),\n",
    "                     shape=(len(ds), len(ds)))\n",
    "    \n",
    "    # compute about-half homophility factor that gaurentees convergences of W power series\n",
    "    c1 = D.sum() + 2\n",
    "    c2 = np.dot(D,D).sum() - 1\n",
    "    h_h = sqrt((-c1+sqrt(c1**2 + 4*c2))\n",
    "               /(8*c2)\n",
    "              )\n",
    "    \n",
    "    # compute system constants ah (from ahD) and ch from (achA)\n",
    "    ah = 4*(h_h**2) /(1-4*(h_h**2))\n",
    "    ch = 2*h_h / (1-4*(h_h**2))\n",
    "    \n",
    "    # create matrix M to form (I - M)^-1\n",
    "    M = ch*A - ah*D\n",
    "    \n",
    "    # expand to power series to compute inverse\n",
    "    inv = I\n",
    "    mat = M\n",
    "    pwr = 1\n",
    "    while mat.data[mat.data.argmax()] > 10**(-9) and pwr < max_pw:\n",
    "        inv = inv + mat\n",
    "        mat = mat * M\n",
    "        pwr = pwr + 1\n",
    "    return inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def DeltaCon(A1, A2):\n",
    "    \n",
    "    # initialize\n",
    "    # prior belief for nodes (doesn't really affect results of deltacon)\n",
    "    p = 0.501\n",
    "    \n",
    "    # Solve the linear BP systems (FaBP): inv corresponds to [I + ah*D - ch*A]^-1\n",
    "    # and multiple by about-half prior beliefs\n",
    "    inv1 = compute_FBP_invs(A1) * (p-0.5)\n",
    "    inv2 = compute_FBP_invs(A2) * (p-0.5)\n",
    "    \n",
    "    # compute the similarities using Matusita distance\n",
    "    diffM = (inv1.sqrt() - inv2.sqrt()) ** 2\n",
    "    # delete diagonal entries -  the influence nodes have on themselves\n",
    "    #diffM = diffM - sp.dia_matrix((diffM.diagonal(), [0]), shape=(diffM.shape[0], diffM.shape[0]))\n",
    "    return 1 / (1 + sqrt(diffM.sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Abb = nx.adjacency_matrix(nx.barbell_graph(10,0))\n",
    "DeltaCon(Abb, Abb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.982427723163\n",
      "0.982427723163\n",
      "0.938245540871\n",
      "0.0441821822924\n"
     ]
    }
   ],
   "source": [
    "AB10 = nx.adjacency_matrix(nx.barbell_graph(5,0))\n",
    "mB10 = nx.barbell_graph(5,0)\n",
    "mB10.remove_edge(5,6)\n",
    "mmB10 = nx.barbell_graph(5,0)\n",
    "mmB10.remove_edge(4,5)\n",
    "print DeltaCon(AB10, nx.adjacency_matrix(mB10))\n",
    "print DeltaCon(nx.adjacency_matrix(mB10), AB10)\n",
    "print DeltaCon(AB10, nx.adjacency_matrix(mmB10))\n",
    "print DeltaCon(nx.adjacency_matrix(mB10), AB10) - DeltaCon(AB10, nx.adjacency_matrix(mmB10))\n",
    "# differnce in paper for DeltaCon_0 is 0.07..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.983029965283\n",
      "0.983029965283\n",
      "0.949798088779\n",
      "0.0332318765038\n"
     ]
    }
   ],
   "source": [
    "# create L10\n",
    "L10 = nx.complete_graph(5)\n",
    "L10.add_path([0,5,6,7,8,9])\n",
    "\n",
    "# create mL10\n",
    "mL10 = nx.complete_graph(5)\n",
    "mL10.add_path([0,5,6,7,8,9])\n",
    "mL10.remove_edge(0,1)\n",
    "# create mmL10\n",
    "mmL10 = nx.complete_graph(5)\n",
    "mmL10.add_path([5,6,7,8,9])\n",
    "\n",
    "print DeltaCon(nx.adjacency_matrix(L10), nx.adjacency_matrix(mL10))\n",
    "print DeltaCon(nx.adjacency_matrix(mL10), nx.adjacency_matrix(L10))\n",
    "print DeltaCon(nx.adjacency_matrix(L10), nx.adjacency_matrix(mmL10))\n",
    "print (DeltaCon(nx.adjacency_matrix(L10), nx.adjacency_matrix(mL10))\n",
    "       - DeltaCon(nx.adjacency_matrix(L10), nx.adjacency_matrix(mmL10)))\n",
    "# differnce in paper for DeltaCon_0 is 0.04..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graph sanity checks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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kcjn9/PPP1KVLF5LJZDRkyBBqaAbDsZll4OAwoJKSEnr33XdpzJgxNHny5Ffe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\nPiK4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aed63d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QhUqlQnR0NEJDQyGVStG2bVssWbIEqampBu2HVV/8daweAFCLFi1o79699M47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\nCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aed6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "nx.draw_shell(mB10)\n",
    "plt.figure(figsize=(3,3))\n",
    "nx.draw_shell(mmB10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mHWDV6ymVVLVzJzXt189hTnHlgvdp3Tg4JKAtLianPn3qPJWtjVGtJsO335KL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\n9v8NPXO0Ss0FKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d97e590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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z3XB1JU3nziZ91hAUZPN9iuCQgKOPD2mSk4mVSlGfY6WSCl9/neYsWUIajcZC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\n/w8scmUl/y70tgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d881710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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T2ZoY1WoyfPcdOXl5WaAyZirep7Xjx7ESMObkmHSAEREpc3PJmJ0tcUXMXLxP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\naBwcjDHRODgYY6JxcDDGROPgYIyJ9v9n9m0VXzYhnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dabc690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "nx.draw_shell(L10)\n",
    "plt.figure(figsize=(3,3))\n",
    "nx.draw_shell(mL10)\n",
    "plt.figure(figsize=(3,3))\n",
    "nx.draw_shell(mmL10)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}