NetworkCreation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code for creating the set of graphs I will be using as the starting points\n",
    "\n",
    "## Note: They should all be similar in some dimension -- number of edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from igraph import *\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_metrics(g):\n",
    "    print(\"Average path length: {}\".format(g.average_path_length()))\n",
    "    print(\"Number of edges: {}\".format(g.ecount()))\n",
    "    print(g.degree_distribution())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_networks(network_type, n, random_seed=23, save=True):\n",
    "    \"\"\"\n",
    "    A function to create adjencency matrices for different network types,\n",
    "    this should probably also demo the placement (to verify that netlogo is right)\n",
    "    \"\"\"\n",
    "    random.seed(random_seed)\n",
    "    for ii in range(n):\n",
    "        if network_type == \"Watts_Strogatz\":\n",
    "            g = Graph.Watts_Strogatz(1, 100, 2, 0.3)\n",
    "            \n",
    "        elif network_type == \"Barabasi\":\n",
    "            g = Graph.Barabasi(100, 2)\n",
    "            \n",
    "        elif network_type == \"Erdos_Renyi\":\n",
    "            g = Graph.Erdos_Renyi(100, m=200)\n",
    "            \n",
    "        elif network_type == \"Lattice\":\n",
    "            g = Graph.Lattice([100], 2)\n",
    "        else:\n",
    "            raise ValueError(\"network_type is not valid\")\n",
    "\n",
    "        \n",
    "        print(\"{}: {}\".format(network_type, ii+1))\n",
    "        graph_metrics(g)\n",
    "        \n",
    "        #plot(g, vertex_size= 8, layout = g.layout_circle())\\\n",
    "        #    .save(\"./networks/initial_networks/images/{}{}.png\".format(network_type, ii+1))\n",
    "            \n",
    "        g.write_adjacency(\"./networks/initial_networks/{}{}.txt\".format(network_type, ii+1))\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Watts_Strogatz: 1\n",
      "Average path length: 3.4957575757575756\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.4495\n",
      "[1, 2): ** (2)\n",
      "[2, 3): ******** (8)\n",
      "[3, 4): *********************************** (35)\n",
      "[4, 5): *********************** (23)\n",
      "[5, 6): *************** (15)\n",
      "[6, 7): ********* (9)\n",
      "[7, 8): ******** (8)\n",
      "Watts_Strogatz: 2\n",
      "Average path length: 3.618585858585859\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.4355\n",
      "[ 1,  2): *** (3)\n",
      "[ 2,  3): ************* (13)\n",
      "[ 3,  4): **************** (16)\n",
      "[ 4,  5): ************************************ (36)\n",
      "[ 5,  6): ******************** (20)\n",
      "[ 6,  7): ******* (7)\n",
      "[ 7,  8): **** (4)\n",
      "[ 8,  9):  (0)\n",
      "[ 9, 10): * (1)\n",
      "Watts_Strogatz: 3\n",
      "Average path length: 3.5299938157081012\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.4284\n",
      "[0, 1): * (1)\n",
      "[1, 2): ** (2)\n",
      "[2, 3): *********** (11)\n",
      "[3, 4): *********************** (23)\n",
      "[4, 5): ************************ (24)\n",
      "[5, 6): ***************************** (29)\n",
      "[6, 7): ***** (5)\n",
      "[7, 8): **** (4)\n",
      "[8, 9): * (1)\n",
      "Watts_Strogatz: 4\n",
      "Average path length: 3.5175757575757576\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.4142\n",
      "[1, 2): ** (2)\n",
      "[2, 3): ************ (12)\n",
      "[3, 4): ************************ (24)\n",
      "[4, 5): **************************** (28)\n",
      "[5, 6): ******************** (20)\n",
      "[6, 7): ********* (9)\n",
      "[7, 8): **** (4)\n",
      "[8, 9): * (1)\n",
      "Watts_Strogatz: 5\n",
      "Average path length: 3.5636363636363635\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.3707\n",
      "[1, 2): ** (2)\n",
      "[2, 3): ********** (10)\n",
      "[3, 4): ************************* (25)\n",
      "[4, 5): ****************************** (30)\n",
      "[5, 6): *********************** (23)\n",
      "[6, 7): ** (2)\n",
      "[7, 8): ******** (8)\n"
     ]
    }
   ],
   "source": [
    "create_networks(\"Watts_Strogatz\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Barabasi: 1\n",
      "Average path length: 2.7107070707070706\n",
      "Number of edges: 197\n",
      "N = 100, mean +- sd: 3.9400 +- 5.2142\n",
      "[ 2,  3): ************************************************************ (60)\n",
      "[ 3,  4): ************** (14)\n",
      "[ 4,  5): ********** (10)\n",
      "[ 5,  6): ** (2)\n",
      "[ 6,  7): ***** (5)\n",
      "[ 7,  8): *** (3)\n",
      "[ 8,  9): * (1)\n",
      "[ 9, 10):  (0)\n",
      "[10, 11):  (0)\n",
      "[11, 12):  (0)\n",
      "[12, 13):  (0)\n",
      "[13, 14):  (0)\n",
      "[14, 15):  (0)\n",
      "[15, 16):  (0)\n",
      "[16, 17): * (1)\n",
      "[17, 18):  (0)\n",
      "[18, 19):  (0)\n",
      "[19, 20):  (0)\n",
      "[20, 21): * (1)\n",
      "[21, 22):  (0)\n",
      "[22, 23): * (1)\n",
      "[23, 24):  (0)\n",
      "[24, 25):  (0)\n",
      "[25, 26):  (0)\n",
      "[26, 27):  (0)\n",
      "[27, 28):  (0)\n",
      "[28, 29):  (0)\n",
      "[29, 30): * (1)\n",
      "[30, 31):  (0)\n",
      "[31, 32):  (0)\n",
      "[32, 33):  (0)\n",
      "[33, 34):  (0)\n",
      "[34, 35):  (0)\n",
      "[35, 36):  (0)\n",
      "[36, 37): * (1)\n",
      "Barabasi: 2\n",
      "Average path length: 2.9123232323232324\n",
      "Number of edges: 197\n",
      "N = 100, mean +- sd: 3.9400 +- 4.3573\n",
      "[ 2,  3): ******************************************************* (55)\n",
      "[ 3,  4): ***************** (17)\n",
      "[ 4,  5): ******** (8)\n",
      "[ 5,  6): **** (4)\n",
      "[ 6,  7): ***** (5)\n",
      "[ 7,  8): *** (3)\n",
      "[ 8,  9): * (1)\n",
      "[ 9, 10):  (0)\n",
      "[10, 11): * (1)\n",
      "[11, 12): ** (2)\n",
      "[12, 13):  (0)\n",
      "[13, 14):  (0)\n",
      "[14, 15):  (0)\n",
      "[15, 16):  (0)\n",
      "[16, 17): * (1)\n",
      "[17, 18):  (0)\n",
      "[18, 19):  (0)\n",
      "[19, 20):  (0)\n",
      "[20, 21):  (0)\n",
      "[21, 22):  (0)\n",
      "[22, 23):  (0)\n",
      "[23, 24): ** (2)\n",
      "[24, 25):  (0)\n",
      "[25, 26):  (0)\n",
      "[26, 27):  (0)\n",
      "[27, 28):  (0)\n",
      "[28, 29): * (1)\n",
      "Barabasi: 3\n",
      "Average path length: 2.8125252525252527\n",
      "Number of edges: 197\n",
      "N = 100, mean +- sd: 3.9400 +- 4.7584\n",
      "[ 2,  3): ************************************************************* (61)\n",
      "[ 3,  4): ********* (9)\n",
      "[ 4,  5): ********* (9)\n",
      "[ 5,  6): ****** (6)\n",
      "[ 6,  7): **** (4)\n",
      "[ 7,  8):  (0)\n",
      "[ 8,  9): ** (2)\n",
      "[ 9, 10): *** (3)\n",
      "[10, 11): * (1)\n",
      "[11, 12): * (1)\n",
      "[12, 13):  (0)\n",
      "[13, 14): * (1)\n",
      "[14, 15):  (0)\n",
      "[15, 16): * (1)\n",
      "[16, 17):  (0)\n",
      "[17, 18):  (0)\n",
      "[18, 19):  (0)\n",
      "[19, 20):  (0)\n",
      "[20, 21):  (0)\n",
      "[21, 22):  (0)\n",
      "[22, 23):  (0)\n",
      "[23, 24):  (0)\n",
      "[24, 25):  (0)\n",
      "[25, 26):  (0)\n",
      "[26, 27): * (1)\n",
      "[27, 28):  (0)\n",
      "[28, 29):  (0)\n",
      "[29, 30):  (0)\n",
      "[30, 31):  (0)\n",
      "[31, 32):  (0)\n",
      "[32, 33):  (0)\n",
      "[33, 34):  (0)\n",
      "[34, 35):  (0)\n",
      "[35, 36):  (0)\n",
      "[36, 37):  (0)\n",
      "[37, 38): * (1)\n",
      "Barabasi: 4\n",
      "Average path length: 2.7834343434343434\n",
      "Number of edges: 197\n",
      "N = 100, mean +- sd: 3.9400 +- 4.8717\n",
      "[ 2,  3): *************************************************************** (63)\n",
      "[ 3,  4): *********** (11)\n",
      "[ 4,  5): ***** (5)\n",
      "[ 5,  6): ******* (7)\n",
      "[ 6,  7): ** (2)\n",
      "[ 7,  8): ** (2)\n",
      "[ 8,  9): * (1)\n",
      "[ 9, 10): *** (3)\n",
      "[10, 11): * (1)\n",
      "[11, 12):  (0)\n",
      "[12, 13): * (1)\n",
      "[13, 14):  (0)\n",
      "[14, 15): * (1)\n",
      "[15, 16):  (0)\n",
      "[16, 17):  (0)\n",
      "[17, 18):  (0)\n",
      "[18, 19):  (0)\n",
      "[19, 20):  (0)\n",
      "[20, 21):  (0)\n",
      "[21, 22):  (0)\n",
      "[22, 23):  (0)\n",
      "[23, 24): * (1)\n",
      "[24, 25):  (0)\n",
      "[25, 26): * (1)\n",
      "[26, 27):  (0)\n",
      "[27, 28):  (0)\n",
      "[28, 29):  (0)\n",
      "[29, 30):  (0)\n",
      "[30, 31):  (0)\n",
      "[31, 32):  (0)\n",
      "[32, 33):  (0)\n",
      "[33, 34):  (0)\n",
      "[34, 35):  (0)\n",
      "[35, 36): * (1)\n",
      "Barabasi: 5\n",
      "Average path length: 2.7173737373737374\n",
      "Number of edges: 197\n",
      "N = 100, mean +- sd: 3.9400 +- 5.2142\n",
      "[ 2,  3): ************************************************************ (60)\n",
      "[ 3,  4): ***************** (17)\n",
      "[ 4,  5): ******** (8)\n",
      "[ 5,  6): **** (4)\n",
      "[ 6,  7): *** (3)\n",
      "[ 7,  8): * (1)\n",
      "[ 8,  9):  (0)\n",
      "[ 9, 10): * (1)\n",
      "[10, 11):  (0)\n",
      "[11, 12): * (1)\n",
      "[12, 13):  (0)\n",
      "[13, 14):  (0)\n",
      "[14, 15):  (0)\n",
      "[15, 16):  (0)\n",
      "[16, 17):  (0)\n",
      "[17, 18):  (0)\n",
      "[18, 19):  (0)\n",
      "[19, 20):  (0)\n",
      "[20, 21):  (0)\n",
      "[21, 22): * (1)\n",
      "[22, 23): * (1)\n",
      "[23, 24):  (0)\n",
      "[24, 25):  (0)\n",
      "[25, 26): ** (2)\n",
      "[26, 27):  (0)\n",
      "[27, 28):  (0)\n",
      "[28, 29):  (0)\n",
      "[29, 30):  (0)\n",
      "[30, 31):  (0)\n",
      "[31, 32):  (0)\n",
      "[32, 33):  (0)\n",
      "[33, 34): * (1)\n"
     ]
    }
   ],
   "source": [
    "create_networks(\"Barabasi\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erdos_Renyi: 1\n",
      "Average path length: 3.427070707070707\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.7753\n",
      "[ 1,  2): ******* (7)\n",
      "[ 2,  3): *********** (11)\n",
      "[ 3,  4): *************************** (27)\n",
      "[ 4,  5): *************** (15)\n",
      "[ 5,  6): ********************* (21)\n",
      "[ 6,  7): ************** (14)\n",
      "[ 7,  8): *** (3)\n",
      "[ 8,  9):  (0)\n",
      "[ 9, 10): * (1)\n",
      "[10, 11):  (0)\n",
      "[11, 12): * (1)\n",
      "Erdos_Renyi: 2\n",
      "Average path length: 3.4188827045969905\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.9695\n",
      "[ 0,  1): * (1)\n",
      "[ 1,  2): ******* (7)\n",
      "[ 2,  3): *************** (15)\n",
      "[ 3,  4): ********************** (22)\n",
      "[ 4,  5): ****************** (18)\n",
      "[ 5,  6): *************** (15)\n",
      "[ 6,  7): ************ (12)\n",
      "[ 7,  8): ****** (6)\n",
      "[ 8,  9): ** (2)\n",
      "[ 9, 10): * (1)\n",
      "[10, 11):  (0)\n",
      "[11, 12): * (1)\n",
      "Erdos_Renyi: 3\n",
      "Average path length: 3.348152920962199\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 2.0986\n",
      "[ 0,  1): *** (3)\n",
      "[ 1,  2): ********* (9)\n",
      "[ 2,  3): ************* (13)\n",
      "[ 3,  4): ******************** (20)\n",
      "[ 4,  5): **************** (16)\n",
      "[ 5,  6): ************** (14)\n",
      "[ 6,  7): *********** (11)\n",
      "[ 7,  8): ******** (8)\n",
      "[ 8,  9): ***** (5)\n",
      "[ 9, 10): * (1)\n",
      "Erdos_Renyi: 4\n",
      "Average path length: 3.3974438260152544\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.7979\n",
      "[0, 1): * (1)\n",
      "[1, 2): *** (3)\n",
      "[2, 3): ***************** (17)\n",
      "[3, 4): ********************** (22)\n",
      "[4, 5): ************************ (24)\n",
      "[5, 6): ************** (14)\n",
      "[6, 7): ******** (8)\n",
      "[7, 8): ***** (5)\n",
      "[8, 9): ****** (6)\n",
      "Erdos_Renyi: 5\n",
      "Average path length: 3.4242742953302483\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 1.9228\n",
      "[ 1,  2): ******* (7)\n",
      "[ 2,  3): *************** (15)\n",
      "[ 3,  4): ********************* (21)\n",
      "[ 4,  5): ********************* (21)\n",
      "[ 5,  6): **************** (16)\n",
      "[ 6,  7): ************ (12)\n",
      "[ 7,  8): ***** (5)\n",
      "[ 8,  9): ** (2)\n",
      "[ 9, 10):  (0)\n",
      "[10, 11):  (0)\n",
      "[11, 12):  (0)\n",
      "[12, 13):  (0)\n",
      "[13, 14): * (1)\n"
     ]
    }
   ],
   "source": [
    "create_networks(\"Erdos_Renyi\", 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lattice: 1\n",
      "Average path length: 12.878787878787879\n",
      "Number of edges: 200\n",
      "N = 100, mean +- sd: 4.0000 +- 0.0000\n",
      "[4, 5): **************************************************************************************************** (100)\n"
     ]
    }
   ],
   "source": [
    "# lol this is a single network, so don't do it 5 times\n",
    "create_networks(\"Lattice\", 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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