Untitled (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6ea4bc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0365b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rand_net(G): #adds one random edge to the graph\n",
    "    a=nx.adjacency_matrix(G).toarray()\n",
    "    choice=np.random.choice(np.arange(0,len(a)),2)\n",
    "    ruler=np.ones((len(a),len(a)))-np.eye(len(a)) #complete graph to check if any empty element exists\n",
    "    check_adj=False\n",
    "    while choice[0]==choice[1] or a[choice[0],choice[1]]==1:\n",
    "        choice=np.random.choice(np.arange(0,len(a)),2)\n",
    "        if (a==ruler).all()==True:\n",
    "            check_adj=True\n",
    "            break\n",
    "        \n",
    "    \n",
    "    a[choice[0],choice[1]]=1;a[choice[1],choice[0]]=1\n",
    "    G_new=nx.from_numpy_array(a,create_using=nx.Graph())\n",
    "    return G_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f99cafd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "size=500\n",
    "l = []\n",
    "C = []\n",
    "for i in range(10000):\n",
    "    if i==0:\n",
    "        G=nx.random_tree(size,seed=0)\n",
    "        A=nx.adjacency_matrix(G).toarray()\n",
    "        np.savetxt('G_r5_0.txt',A)\n",
    "        l.append(nx.average_shortest_path_length(G))\n",
    "        C.append(nx.average_clustering(G))\n",
    "    else:\n",
    "        G=rand_net(G)\n",
    "        A=nx.adjacency_matrix(G).toarray()\n",
    "        np.savetxt('G_r5_'+str(i)+'.txt',A)\n",
    "        l.append(nx.average_shortest_path_length(G))\n",
    "        C.append(nx.average_clustering(G))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d09a152d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       " 3.6825490981963926,\n",
       " 3.681931863727455,\n",
       " 3.6799038076152306,\n",
       " 3.6787414829659317,\n",
       " 3.6776513026052102,\n",
       " 3.6757755511022046,\n",
       " 3.6742925851703405,\n",
       " 3.673122244488978,\n",
       " ...]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9e60c89e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96aeee88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Shortest_Path_length')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(l)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('Shortest_Path_length')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19b3113b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a573424",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "len(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f5710eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average_Clustering')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(C)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('Average_Clustering')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2e6b4f5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Shortest_Path_length')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(l),plt.plot(C)\n",
    "plt.xlabel('number of added edges to graph');plt.ylabel('Shortest_Path_length')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "962a6e81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}