Analysis_KClub.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node Degree\n",
      "[16, 9, 10, 6, 3, 4, 4, 4, 5, 2, 3, 1, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 2, 5, 3, 3, 2, 4, 3, 4, 4, 6, 12, 17]\n"
     ]
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "G = nx.karate_club_graph()\n",
    "deg=[]\n",
    "#nodes degree \n",
    "print(\"Node Degree\")\n",
    "for v in G:\n",
    "    #print('%s %s' % (v, G.degree(v)))\n",
    "    deg.append(G.degree(v))\n",
    "    \n",
    "\n",
    "#nx.draw_circular(G, with_labels=True)\n",
    "#plt.show()\n",
    "print(deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DegreeView({0: 16, 1: 9, 2: 10, 3: 6, 4: 3, 5: 4, 6: 4, 7: 4, 8: 5, 9: 2, 10: 3, 11: 1, 12: 2, 13: 5, 14: 2, 15: 2, 16: 2, 17: 2, 18: 2, 19: 3, 20: 2, 21: 2, 22: 2, 23: 5, 24: 3, 25: 3, 26: 2, 27: 4, 28: 3, 29: 4, 30: 4, 31: 6, 32: 12, 33: 17})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.degree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.588235294117647\n"
     ]
    }
   ],
   "source": [
    "degrees = [val for (node, val) in G.degree()]\n",
    "sum_of_edges = sum(degrees)\n",
    "\n",
    "Avg_deg=sum_of_edges/len(G)\n",
    "\n",
    "print(Avg_deg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking bipartite graph\n",
    "from networkx.algorithms import bipartite\n",
    "\n",
    "bipartite.is_bipartite(G) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.15,\n",
       " 1: 0.3333333333333333,\n",
       " 2: 0.24444444444444444,\n",
       " 3: 0.6666666666666666,\n",
       " 4: 0.6666666666666666,\n",
       " 5: 0.5,\n",
       " 6: 0.5,\n",
       " 7: 1.0,\n",
       " 8: 0.5,\n",
       " 9: 0,\n",
       " 10: 0.6666666666666666,\n",
       " 11: 0,\n",
       " 12: 1.0,\n",
       " 13: 0.6,\n",
       " 14: 1.0,\n",
       " 15: 1.0,\n",
       " 16: 1.0,\n",
       " 17: 1.0,\n",
       " 18: 1.0,\n",
       " 19: 0.3333333333333333,\n",
       " 20: 1.0,\n",
       " 21: 1.0,\n",
       " 22: 1.0,\n",
       " 23: 0.4,\n",
       " 24: 0.3333333333333333,\n",
       " 25: 0.3333333333333333,\n",
       " 26: 1.0,\n",
       " 27: 0.16666666666666666,\n",
       " 28: 0.3333333333333333,\n",
       " 29: 0.6666666666666666,\n",
       " 30: 0.5,\n",
       " 31: 0.2,\n",
       " 32: 0.19696969696969696,\n",
       " 33: 0.11029411764705882}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5706384782076823"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.48484848484848486,\n",
       " 1: 0.2727272727272727,\n",
       " 2: 0.30303030303030304,\n",
       " 3: 0.18181818181818182,\n",
       " 4: 0.09090909090909091,\n",
       " 5: 0.12121212121212122,\n",
       " 6: 0.12121212121212122,\n",
       " 7: 0.12121212121212122,\n",
       " 8: 0.15151515151515152,\n",
       " 9: 0.06060606060606061,\n",
       " 10: 0.09090909090909091,\n",
       " 11: 0.030303030303030304,\n",
       " 12: 0.06060606060606061,\n",
       " 13: 0.15151515151515152,\n",
       " 14: 0.06060606060606061,\n",
       " 15: 0.06060606060606061,\n",
       " 16: 0.06060606060606061,\n",
       " 17: 0.06060606060606061,\n",
       " 18: 0.06060606060606061,\n",
       " 19: 0.09090909090909091,\n",
       " 20: 0.06060606060606061,\n",
       " 21: 0.06060606060606061,\n",
       " 22: 0.06060606060606061,\n",
       " 23: 0.15151515151515152,\n",
       " 24: 0.09090909090909091,\n",
       " 25: 0.09090909090909091,\n",
       " 26: 0.06060606060606061,\n",
       " 27: 0.12121212121212122,\n",
       " 28: 0.09090909090909091,\n",
       " 29: 0.12121212121212122,\n",
       " 30: 0.12121212121212122,\n",
       " 31: 0.18181818181818182,\n",
       " 32: 0.36363636363636365,\n",
       " 33: 0.5151515151515151}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree_centrality(G)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.number_connected_components(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(nx.connected_components(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EdgeView([(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (9, 33), (13, 33), (14, 32), (14, 33), (15, 32), (15, 33), (18, 32), (18, 33), (19, 33), (20, 32), (20, 33), (22, 32), (22, 33), (23, 25), (23, 27), (23, 29), (23, 32), (23, 33), (24, 25), (24, 27), (24, 31), (25, 31), (26, 29), (26, 33), (27, 33), (28, 31), (28, 33), (29, 32), (29, 33), (30, 32), (30, 33), (31, 32), (31, 33), (32, 33)])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.node_connectivity(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.minimum_node_cut(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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_networkx(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.edge_connectivity(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(11, 0)}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.minimum_edge_cut(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = nx.bfs_tree(G, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OutEdgeView([(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 30), (2, 9), (2, 27), (2, 28), (2, 32), (5, 16), (8, 33), (31, 24), (31, 25), (27, 23), (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 29), (33, 26)])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0,\n",
       " 1: 1,\n",
       " 2: 1,\n",
       " 3: 1,\n",
       " 4: 1,\n",
       " 5: 1,\n",
       " 6: 1,\n",
       " 7: 1,\n",
       " 8: 1,\n",
       " 10: 1,\n",
       " 11: 1,\n",
       " 12: 1,\n",
       " 13: 1,\n",
       " 17: 1,\n",
       " 19: 1,\n",
       " 21: 1,\n",
       " 31: 1,\n",
       " 30: 2,\n",
       " 9: 2,\n",
       " 27: 2,\n",
       " 28: 2,\n",
       " 32: 2,\n",
       " 16: 2,\n",
       " 33: 2,\n",
       " 24: 2,\n",
       " 25: 2,\n",
       " 23: 3,\n",
       " 14: 3,\n",
       " 15: 3,\n",
       " 18: 3,\n",
       " 20: 3,\n",
       " 22: 3,\n",
       " 29: 3,\n",
       " 26: 3}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.shortest_path_length(G,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.408199643493761"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_shortest_path_length(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 3, 1: 3, 2: 3, 3: 3, 4: 4, 5: 4, 6: 4, 7: 4, 8: 3, 9: 4, 10: 4, 11: 4, 12: 4, 13: 3, 14: 5, 15: 5, 16: 5, 17: 4, 18: 5, 19: 3, 20: 5, 21: 4, 22: 5, 23: 5, 24: 4, 25: 4, 26: 5, 27: 4, 28: 4, 29: 5, 30: 4, 31: 3, 32: 4, 33: 4}\n"
     ]
    }
   ],
   "source": [
    "print(nx.eccentricity(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(nx.radius(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(nx.diameter(G))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14, 15, 16, 18, 20, 22, 23, 26, 29]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.periphery(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 8, 13, 19, 31]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.center(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5706384782076823"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.average_clustering(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node Degree\n"
     ]
    }
   ],
   "source": [
    "deg=[]\n",
    "#nodes degree \n",
    "print(\"Node Degree\")\n",
    "for v in G:\n",
    "    #print('%s %s' % (v, G.degree(v)))\n",
    "    deg.append(G.degree(v))\n",
    "\n",
    "clustC=nx.clustering(G)\n",
    "clustC=list(clustC.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "\n",
    "\n",
    "plt.scatter(deg, clustC , label= \"stars\", color= \"green\",  \n",
    "            marker= \"*\", s=30) \n",
    "\n",
    "# naming the x axis \n",
    "plt.xlabel('Degree') \n",
    "# naming the y axis \n",
    "plt.ylabel('Clustering Coefficient') \n",
    "  \n",
    "# giving a title to my graph \n",
    "plt.title('Nodes degree and clustering coefficient') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from networkx.algorithms import bipartite\n",
    "bipartite.is_bipartite(G) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}