310112011_HW5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85f4a5ed-a66c-4227-abd0-e160938ec681",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a79b9a0-9f59-4c9d-81e6-922efadad13c",
   "metadata": {},
   "source": [
    "### Find  1  isomorphic  graph  of  network  1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6cc3e24-8e62-46b3-9ef6-71b7cf020955",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_edgelist(\"Network 1.txt\", create_using=nx.MultiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b02ecdf-2c6c-44c5-a5cb-ee2a5c7a39e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G, with_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "03c0cc75-123a-43d0-9729-7f8b4b1047c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "G_iso = nx.read_edgelist(\"iso_Network 1.txt\", create_using=nx.MultiGraph())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e8aade62-52a5-4ac2-91e4-d12edf172d3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx.draw(G_iso, with_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef7b6a2a-f64c-48fa-8ab5-a479766b98cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.is_isomorphic(G, G_iso)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b961a8d8-98ba-42dd-9b95-938d42acc613",
   "metadata": {},
   "source": [
    "### List  the  deck  of  network  1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9bc4d1c7-52d5-4f5c-9542-8b21c80b5ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network 1: Decks show by adjacency list format\n",
      "#Deck 0\n",
      "e h g c f d\n",
      "g h d f\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 1\n",
      "i\n",
      "g h d f\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 2\n",
      "i e\n",
      "e h c f d\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 3\n",
      "i e\n",
      "e g c f d\n",
      "g d f\n",
      "d f\n",
      "f\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 4\n",
      "i e\n",
      "e h g c f\n",
      "g h f\n",
      "h f\n",
      "f\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 5\n",
      "i e\n",
      "e h g c d\n",
      "g h d\n",
      "h d\n",
      "d\n",
      "c b\n",
      "b a\n",
      "a\n",
      "#Deck 6\n",
      "i e\n",
      "e h g f d\n",
      "g h d f\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "b a\n",
      "a\n",
      "#Deck 7\n",
      "i e\n",
      "e h g c f d\n",
      "g h d f\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "c\n",
      "a\n",
      "#Deck 8\n",
      "i e\n",
      "e h g c f d\n",
      "g h d f\n",
      "h f d\n",
      "d f\n",
      "f\n",
      "c b\n",
      "b\n"
     ]
    }
   ],
   "source": [
    "all_nodes = list(G.nodes)\n",
    "print(\"Network 1: Decks show by adjacency list format\")\n",
    "for i in range(len(all_nodes)):\n",
    "    deck = G.copy()\n",
    "    deck.remove_node(all_nodes[i])\n",
    "    print(\"#Deck %s\" %i)\n",
    "    for line in nx.generate_adjlist(deck):\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feaf3432-7a33-45a1-874a-87721962f343",
   "metadata": {},
   "source": [
    "### Check  if  the  decks  of  the  isomorphic  graphs  you  found are  identical  to  the  deck  of  network  1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "02932ecf-fe43-430f-8f20-5f03a0ce0993",
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_iso(G_iso_deck):\n",
    "    all_nodes = list(G.nodes)\n",
    "    for i in range(len(all_nodes)):\n",
    "        deck = G.copy()\n",
    "        deck.remove_node(all_nodes[i])\n",
    "        if nx.is_isomorphic(deck, G_iso_deck):\n",
    "            return True\n",
    "    else:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5cc69bd8-b51b-4d77-83c2-0c7566a416a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "isomorphic graphs!\n"
     ]
    }
   ],
   "source": [
    "iso_all_nodes = list(G_iso.nodes)\n",
    "check = len(iso_all_nodes)\n",
    "for i in range(len(iso_all_nodes)):\n",
    "    G_iso_deck = G_iso.copy()\n",
    "    G_iso_deck.remove_node(iso_all_nodes[i])\n",
    "    if check_iso(G_iso_deck) == False:\n",
    "        check -= 1\n",
    "        print(\"Not isomorphic graphs!\")\n",
    "if check == len(iso_all_nodes):\n",
    "    print(\"isomorphic graphs!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ba2f013-3a9a-4a8d-95bc-8bd473cb215b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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