Edge Weight and Network structure.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Edge Weight and Network structure</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "            @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from pprint import pprint\n",
    "from itertools import combinations\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sqlite3\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "import networkx as nx\n",
    "\n",
    "import tqdm as tq\n",
    "from tqdm import tqdm\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by print out the versions of the libraries we're using for future reference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 21.3.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 58d07878bc88b565bc1569df8f5a33c4ae028a46\n",
      "\n",
      "pandas    : 1.1.3\n",
      "matplotlib: 3.3.2\n",
      "networkx  : 2.5\n",
      "json      : 2.0.9\n",
      "watermark : 2.1.0\n",
      "sqlite3   : 2.6.0\n",
      "numpy     : 1.19.2\n",
      "tqdm      : 4.62.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load default figure style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Toy Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us write out a simple toy network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_edges_from([\n",
    "    (0, 1),\n",
    "    (0, 2),\n",
    "    (0, 3),\n",
    "    (0, 4),\n",
    "    (0, 5),\n",
    "    (3, 2),\n",
    "    (3, 4),\n",
    "    (1, 6),\n",
    "    (2, 6),\n",
    "    (3, 6),\n",
    "    (4, 6),\n",
    "    (5, 6),\n",
    "])\n",
    "\n",
    "pos = {\n",
    "    0:(-1, 0),\n",
    "    1:(0, 1),\n",
    "    2:(0, .5),\n",
    "    3:(0, 0),\n",
    "    4:(0, -.5),\n",
    "    5:(0, -1),\n",
    "    6:(1, 0),\n",
    "}\n",
    "\n",
    "node_colors = np.array(colors)[[0, 3, 1, 2, 1, 3, 0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And make a quick visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: Text(-1, 0, '0'),\n",
       " 1: Text(0, 1, '1'),\n",
       " 2: Text(0, 0.5, '2'),\n",
       " 3: Text(0, 0, '3'),\n",
       " 4: Text(0, -0.5, '4'),\n",
       " 5: Text(0, -1, '5'),\n",
       " 6: Text(1, 0, '6')}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, figsize=(4, 4))\n",
    "ax.set_aspect(1)\n",
    "nx.draw(G, pos=pos, node_color=None)\n",
    "nx.draw_networkx_nodes(G, pos=pos, node_color=node_colors)\n",
    "nx.draw_networkx_labels(G, pos=pos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neighborhood Overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the network above, it's clear that several nodes have similar properties. 0 and 6 have the same set of neighbors, as do 5 and 1, 4 and 2, etc. The only node that is truly unique is node number 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We formalize this intuition using the overlap:\n",
    "$$ O_{i,j}=\\frac{\\mathrm{common~neighbors}_{i, j}}{\\mathrm{total~neighbors}_{i, j}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "which we implement in Python as"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_overlap(G, full=False):\n",
    "    overlap = {}\n",
    "    NN = {}\n",
    "    \n",
    "    # Consider all pairs or only existing edges \n",
    "    # depending on the value of full\n",
    "    edges = (combinations(G.nodes(), 2) \n",
    "             if full else G.edges())\n",
    "    \n",
    "    # For each pair of nodes we're considering\n",
    "    for node_i, node_j in edges:\n",
    "        # Get neighborhoods making sure we don't repeatedly \n",
    "        # evaluate the same neighborhood\n",
    "        if node_i not in NN:\n",
    "            NN[node_i] = set(nx.neighbors(G, node_i))\n",
    "\n",
    "        if node_j not in NN:\n",
    "            NN[node_j] = set(nx.neighbors(G, node_j))\n",
    "        \n",
    "        # Compute the overlap excluding the nodes themselves\n",
    "        common_neighbors = (NN[node_i] & NN[node_j]) - set([node_i, node_j])\n",
    "        total_neighbors = (NN[node_i] | NN[node_j])  - set([node_i, node_j])\n",
    "        \n",
    "        if len(total_neighbors) > 0:\n",
    "            overlap[(node_i, node_j)] = len(common_neighbors)/len(total_neighbors)\n",
    "        else:\n",
    "            overlap[(node_i, node_j)] = 0\n",
    "    \n",
    "    return overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this simple network we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(0, 1)</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 2)</th>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 3)</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 4)</th>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(0, 5)</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 6)</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 6)</th>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3, 4)</th>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(3, 6)</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(4, 6)</th>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(5, 6)</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0\n",
       "(0, 1)  0.00\n",
       "(0, 2)  0.20\n",
       "(0, 3)  0.40\n",
       "(0, 4)  0.20\n",
       "(0, 5)  0.00\n",
       "(1, 6)  0.00\n",
       "(2, 3)  0.67\n",
       "(2, 6)  0.20\n",
       "(3, 4)  0.67\n",
       "(3, 6)  0.40\n",
       "(4, 6)  0.20\n",
       "(5, 6)  0.00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(get_overlap(G), orient='index').round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where we easily find that the nodes with the highest neighborhood overlap are 2 and 3 and 3 and 4, both with 2/3, while many other pairs of nodes (like 0 and 1) have no common neighbors at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facebook-like Social Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('data/OF_one-mode_weightedmsg_sum.txt', sep=' ', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.Graph()\n",
    "G.add_weighted_edges_from(data.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network has "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "897"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N = G.number_of_nodes()\n",
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71380"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E = G.number_of_edges()\n",
    "E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_connected(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compute the overlap along all edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap = get_overlap(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And plot its distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap_dist = pd.DataFrame(Counter(np.round(list(overlap.values()), 2)).items())\n",
    "overlap_dist[1] /= E\n",
    "overlap_dist.columns = ['Overlap', 'P(Overlap)']\n",
    "overlap_dist.sort_values('Overlap', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '$Overlap$')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = overlap_dist.plot(x='Overlap', y='P(Overlap)', legend=None)\n",
    "ax.set_ylabel(r'$P\\left(Overlap\\right)$')\n",
    "ax.set_xlabel(r'$Overlap$')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, most edges have relatively low overlaps (<0.4) with just a few edges connecting nodes with high degrees of overlaps (~1.0). On the other hand, the weight distribution is significantly broad tailed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "weight_distribution = pd.DataFrame(Counter([w for i, j, w in G.edges(data=\"weight\")]).items())\n",
    "weight_distribution.sort_values(0, inplace=True)\n",
    "weight_distribution[1] /= E\n",
    "weight_distribution.columns = ['w', 'P(w)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = weight_distribution.plot(x='w', y='P(w)', legend=None)\n",
    "ax.set_ylabel(r'$P\\left(w\\right)$')\n",
    "ax.set_xlabel(r'$w$')\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "[label.set_y(-0.01) for label in ax.xaxis.get_majorticklabels()];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite this difference in distribution, there is an impressive and important correlation between these two quantities. In particular, edges with higher weights connect nodes with higher degree of overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "overlap_weight = {}\n",
    "\n",
    "for node_i, node_j, weight in G.edges(data=\"weight\"):\n",
    "    weight = np.power(2, int(np.log2(weight+1)))\n",
    "    \n",
    "    if weight not in overlap_weight:\n",
    "        overlap_weight[weight] = []\n",
    "        \n",
    "    overlap_weight[weight].append(overlap[(node_i, node_j)])\n",
    "    \n",
    "for weight in overlap_weight:\n",
    "    overlap_weight[weight] = (weight, \n",
    "                              np.mean(overlap_weight[weight]), \n",
    "                               np.std(overlap_weight[weight]))\n",
    "    \n",
    "overlap_weight = pd.DataFrame(overlap_weight.values())\n",
    "overlap_weight.sort_values(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the overlap as a function of the weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = overlap_weight.plot(x=0, y=1, yerr=2, legend=False, zorder=-1, lw=2)\n",
    "overlap_weight.plot.scatter(x=0, y=1, color=colors[1], s=100, ax=ax, legend=False, zorder=2)\n",
    "ax.set_xlabel('Weight')\n",
    "ax.set_ylabel('Overlap')\n",
    "ax.set_xscale('log')\n",
    "[label.set_y(-0.01) for label in ax.xaxis.get_majorticklabels()];"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Illustrating a clear increase of the degree of overlap as the edge weight increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
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