{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random as rnd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#this function generates the BA scale free graph and returns how the max degree increased trough time\n",
    "#parametrs: t=time, m0 initial node number (FC), m number of links for every node \n",
    "#returns: max_degrees\n",
    "def get_SF_maxdegree(t, m=2, m0=2):\n",
    "    #degree dict\n",
    "    degrees = {}\n",
    "    #max degree node in t\n",
    "    mdn = 0\n",
    "    #max degree in t\n",
    "    md  = m0-1\n",
    "    # contains the nodes as many times as it's degree\n",
    "    nodeList = []\n",
    "    # max degrees\n",
    "    max_degrees = []\n",
    "    #initialize the FC graph at t=0\n",
    "    for i in range(m0):\n",
    "        # connecting the node with all of the other nodes\n",
    "        #edges[i] = set([j for j in range(m0) if i!=j])\n",
    "        # adding the i node to nodeList as many times as it's degree\n",
    "        nodeList.extend([i]*m0)\n",
    "        degrees[i] = m0\n",
    "    #iterating trough time, time step also used as node index\n",
    "    #but I already have 0,..., m0 nodes, so I shift the loop\n",
    "    for i in range(m0,t+m0):\n",
    "        #selected nodes: this nodes will be connected to the new node\n",
    "        selected_nodes = []\n",
    "        #we try to select node until we have selected m node\n",
    "        while len(selected_nodes)!=m:\n",
    "            # selecting a node, nodeList contains a node as many times as it's degree. \n",
    "            # So the probability of selecting node is propto it's degree.\n",
    "            sn = nodeList[rnd.randint(0, len(nodeList)-1)]\n",
    "            # if we already selected this node, we don't select it again, \n",
    "            # because we can't connect multiple times the same two node\n",
    "            if sn not in selected_nodes:\n",
    "                # adding the selected node to the selected_nodes list\n",
    "                selected_nodes.append(sn)\n",
    "                # if sn is the max degree node, then de max degree is increased now with 1\n",
    "                if sn==mdn:\n",
    "                    md += 1\n",
    "                degrees[sn] += 1\n",
    "                # if the new node degree is bigger then md then we have a new max degree node\n",
    "                if degrees[sn]>md:\n",
    "                    md  = degrees[sn]\n",
    "                    mdn = sn\n",
    "        # adding the selected nodes to nodeList\n",
    "        nodeList.extend(selected_nodes)\n",
    "        # adding the new node to the nodeList as many times as it's degree, which is m\n",
    "        nodeList.extend([i]*m)\n",
    "        # adding the new node to degrees dict\n",
    "        degrees[i] = m\n",
    "        # if the new node degree's > max degree: we have a new max degree node\n",
    "        if m>md:\n",
    "            md  = m\n",
    "            mdn = i\n",
    "        max_degrees.append(md)\n",
    "    return max_degrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# getting the degree increase for t=10^6\n",
    "degrees = get_SF_maxdegree(1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# creating the time list\n",
    "time = [i for i in range(len(degrees))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f71c05e4a8>]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P3nor7VZQVrNmEEIKDDp3ht13T33r+JOCVKP5V4AkSZJUhBYtgvfegyefTNsUQhoh0LMn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\nYlggSZIkSZJaMSyQJEmSJEmt/H8RYXT4bf4OHwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f71bfd19e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot\n",
    "plt.figure(0, (12,7))\n",
    "plt.xlim([-max(time)*0.02, max(time)*1.02])\n",
    "plt.ylim([-max(degrees)*0.02, max(degrees)*1.02])\n",
    "plt.xlabel(\"Time\", fontsize=18)\n",
    "plt.ylabel(\"Max degree\", fontsize=18)\n",
    "plt.plot(time, degrees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": 1
}