graph_vislz.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Tr_centrality_measure\n",
    "import Graph_Entropy_measure\n",
    "import Selection_Algorithm\n",
    "import Constraint_Coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#G = nx.karate_club_graph()\n",
    "fh=open(\"DataSet/dolphins.txt\", 'rb')\n",
    "H= nx.read_weighted_edgelist(fh,nodetype=int, encoding='utf-8')\n",
    "fh.close()\n",
    "#H = nx.convert_node_labels_to_integers(G, first_label=0, ordering=\"default\", label_attribute = None) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations\n",
    "def nodes_in_triangle(H, n):\n",
    "    \"\"\"\n",
    "    Returns the nodes in a graph `G` that are involved in a triangle relationship with the node `n`.\n",
    "    \"\"\"\n",
    "    triangle_nodes = set([n])\n",
    "\n",
    "    # Iterate over all possible triangle relationship combinations\n",
    "    for n1, n2 in combinations(H.neighbors(n), 2):\n",
    "\n",
    "        # Check if n1 and n2 have an edge between them\n",
    "        if H.has_edge(n1, n2):\n",
    "\n",
    "            # Add n1 to triangle_nodes\n",
    "            triangle_nodes.add(n1)\n",
    "\n",
    "            # Add n2 to triangle_nodes\n",
    "            triangle_nodes.add(n2)\n",
    "\n",
    "    return triangle_nodes\n",
    "#equetion 1:selection constrain\n",
    "def selection_constrain_of(i):\n",
    "     # degree of N_i:\n",
    "        N_i = subgraph_of(i).number_of_nodes()\n",
    "        # sum(sdeg_j):\n",
    "        sum_sdeg_j = subgraph_of(i).number_of_edges()\n",
    "        # number of triangles: nodes\n",
    "        NT_i = nx.triangles(H,i)\n",
    "        if NT_i > 1:\n",
    "        #equetion 1:selection constrain        \n",
    "            TR_i = (N_i-(sum_sdeg_j/2))            \n",
    "        return  TR_i\n",
    "    \n",
    "#Selection Algorithm\n",
    "def Selection_Algorithm():\n",
    "    # main part of AL loop\n",
    "    i=0\n",
    "    valid_set = []\n",
    "    for i in H.nodes: \n",
    "        subgraph = subgraph_of(i)\n",
    "        # degree of Ni:\n",
    "        N_i = subgraph.number_of_nodes()\n",
    "        # number of triangles: nodes\n",
    "        NT_i = nx.triangles(H,i)\n",
    "        # Extract the nodes of interest: nodes\n",
    "        nodes = [n for n, d in subgraph.nodes(data=True)]\n",
    "        # Create the set of nodes: nodeset\n",
    "        nodeset = set(nodes)\n",
    "        #equetion 1:selection constrain\n",
    "        if NT_i > 1:\n",
    "            TR_i = selection_constrain_of(i)\n",
    "            i+=1        \n",
    "            list1 = i, NT_i     \n",
    "            valid_set.append(list1)\n",
    "                \n",
    "    return  valid_set\n",
    "\n",
    "\n",
    "# In[ ]:\n",
    "def sel_subgraphs():\n",
    "    sel_subgraphs = []\n",
    "    for i in H:\n",
    "        sel_subgraph = H.subgraph(nodes_in_triangle(H, i))\n",
    "        NT_i = nx.triangles(H,i)\n",
    "        if NT_i > 1:            \n",
    "            row = i        \n",
    "            sel_subgraphs.append(row)\n",
    "    return  sel_subgraphs\n",
    "\n",
    "# In[ ]:\n",
    "#return subgraphs\n",
    "def subgraph_of(i):\n",
    "    subgraph = H.subgraph(nodes_in_triangle(H, i))\n",
    "    return  subgraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# G = nx.Graph()   # or DiGraph, MultiGraph, MultiDiGraph, etc\n",
    "I = H.copy()\n",
    "J = H.copy()\n",
    "ebunch =list(subgraph_of(3).edges)\n",
    "I.remove_edges_from(ebunch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "#H = G.remove_node(0)\n",
    "#G.remove_edge(Selection_Algorithm.subgraph_of(33).edges())\n",
    "#nx.draw(I, with_labels = True)\n",
    "plt.show()\n",
    "#plt.savefig(\"out.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "#H = G.remove_node(0)\n",
    "#G.remove_edge(Selection_Algorithm.subgraph_of(33).edges())\n",
    "nx.draw(subgraph_of(15), with_labels = True)\n",
    "plt.show()\n",
    "#plt.savefig(\"out.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "#H = G.remove_node(0)\n",
    "#G.remove_edge(Selection_Algorithm.subgraph_of(33).edges())\n",
    "#nx.draw(subgraph_of(4384), with_labels = True)\n",
    "plt.show()\n",
    "#plt.savefig(\"out.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "#H = G.remove_node(0)\n",
    "#G.remove_edge(Selection_Algorithm.subgraph_of(33).edges())\n",
    "#nx.draw(subgraph_of(4327), with_labels = True)\n",
    "plt.show()\n",
    "#plt.savefig(\"out.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.degree(46)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cent = pd.read_csv('result/dolphins--/propose/propose_result_table_dolphins.csv')\n",
    "cent1 = cent.drop(['Unnamed: 0'], axis=1)\n",
    "#cent2=[tuple(x) for x in cent1.to_records(index=False)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}