BA Model and Preferential Attachment.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=160px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> <h1>Barabasi-Albert Model and Preferential Attachment</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",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "from itertools import chain\n",
    "import networkx as nx\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Watermark the notebook with current versions of all loaded libraries"
   ]
  },
  {
   "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     : 20.6.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: bd0d55512d2130c113fdbc721eea484e3e7b1f5b\n",
      "\n",
      "numpy     : 1.19.2\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",
      "\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": [
    "## Graph Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Barabasi-Albert model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of the Barabasi-Albert model in straightforward. Here we start with m0+1 fully connected nodes and add m0 new edges at each time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def barabasi_albert_graph(N, m0=3):    \n",
    "    # Store all the edges in a list\n",
    "    edges = []\n",
    "    \n",
    "    # For every starting node\n",
    "    for i in range(m0):\n",
    "        \n",
    "        # Create an edge with every node with larger id\n",
    "        for j in range(i+1, m0):\n",
    "            # Edges are store as 'flat' sequences of edges\n",
    "            edges.append(i)\n",
    "            edges.append(j)\n",
    "    \n",
    "    # For all the other nodes\n",
    "    for node_i in range(m0, N):\n",
    "        new_edges = set()\n",
    "        \n",
    "        # Make sure we don't add duplicated edges\n",
    "        while len(new_edges) < m0:\n",
    "            pos = np.random.randint(len(edges))\n",
    "            node_j = edges[pos]\n",
    "            \n",
    "            if node_i != node_j: \n",
    "                new_edges.add(node_j)\n",
    "\n",
    "        # Add the new edges to the edge list\n",
    "        edges.extend(chain.from_iterable(zip([node_i]*m0, new_edges)))\n",
    "    \n",
    "    # Reshape the edge list as two columns\n",
    "    edges = np.array(edges).reshape(-1,2)\n",
    "    \n",
    "    # Create an empty graph\n",
    "    G = nx.Graph()\n",
    "    \n",
    "    # Add edges to the Graph\n",
    "    G.add_edges_from(edges)\n",
    "\n",
    "    return G"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate a simple network with this procedure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N=1_000_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "BA = barabasi_albert_graph(N, m0=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And check that we have the right number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BA.number_of_nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the expected number of edges $$E=N_0\\frac{N_0-1}{2}+m_0t$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4999985"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BA.number_of_edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the degree distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pk = pd.DataFrame(Counter(dict(BA.degree()).values()).items(), \n",
    "                  columns=['k', 'Pk']).set_index('k').sort_index()\n",
    "Pk.Pk/=N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a straight line to guide the eye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.arange(1, 10000, dtype='float')\n",
    "Pk_theory = 1000*k**(-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which we can plot side by side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f92304a3370>"
      ]
     },
     "execution_count": 11,
     "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 = Pk.plot(legend=None)\n",
    "ax.plot(k, Pk_theory, linestyle='--')\n",
    "ax.set_xlabel('k')\n",
    "ax.set_ylabel('P(k)')\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "ax.set_ylim(.5*1e-6, 1)\n",
    "ax.set_xlim(1, 1e4)\n",
    "\n",
    "ax.axvline(x=5, linestyle=':', color=colors[3], linewidth=2)\n",
    "\n",
    "for tick_label in ax.xaxis.get_majorticklabels():\n",
    "    tick_label.set_y(-.01)\n",
    "\n",
    "ax.legend(['Pk', 'k=-3', r'$m_0$'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naturally, the degrees start at the value of $m_0$ we set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also plot the degree as a function of the node ID to illustrate how the process is unfair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "degrees = pd.DataFrame(BA.degree(), columns=['node', 'k']).set_index('node')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'node$_i$')"
      ]
     },
     "execution_count": 13,
     "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 = degrees.plot(lw=1, legend=None)\n",
    "ax.set_yscale('log')\n",
    "ax.set_xscale('log')\n",
    "\n",
    "for tick_label in ax.xaxis.get_majorticklabels():\n",
    "    tick_label.set_y(-.01)\n",
    "    \n",
    "ax.set_ylabel(r'$k_i$')\n",
    "ax.set_xlabel(r'node$_i$')"
   ]
  },
  {
   "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>"
   ]
  }
 ],
 "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}