WBFM_research.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Imports**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "from sklearn.neighbors import kneighbors_graph\n",
    "from tqdm import tqdm_notebook, tqdm\n",
    "from collections import Counter, defaultdict\n",
    "import pickle\n",
    "import community\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "from plotly.offline import iplot, init_notebook_mode, plot\n",
    "init_notebook_mode(connected=True)\n",
    "from utils import *\n",
    "import numpy as np\n",
    "import scipy.sparse\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loading graphs and vectors datasets**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cora dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1cd31235469d429a889e838de3b32992",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=2708.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.read_edgelist('../Datasets/Cora/cora_edges.txt')\n",
    "\n",
    "vect = {}\n",
    "labels = dict()\n",
    "sim_file = open(\"../Datasets/Cora/cora_features\",\"r\")\n",
    "for line in tqdm_notebook(sim_file, total=len(G.nodes()), leave=False):\n",
    "    node_id = int(line.split()[0])\n",
    "    features = np.asarray(list(map(float,line.split()[1:-1])))\n",
    "    labels[str(node_id)] = (line.split()[-1])\n",
    "    vect.update({str(node_id):features})\n",
    "sim_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt_labels = {}\n",
    "for n in G.nodes():\n",
    "    gt_labels[n] = labels[n]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sinanet dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=3467.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.read_edgelist('../Datasets/Sinanet/Sinanet-master/edge.txt')\n",
    "\n",
    "vect = {}\n",
    "sim_file = open(\"../Datasets/Sinanet/Sinanet-master/content.txt\",\"r\")\n",
    "line_iter = 1\n",
    "for line in tqdm_notebook(sim_file, total=len(G.nodes()), leave=False):\n",
    "    features = np.asarray(list(map(float,line.split())))\n",
    "    vect.update({str(line_iter):features})\n",
    "    line_iter +=1\n",
    "sim_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de947a764bd84198970446ba8c724678",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=3467.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "label_dict = dict()\n",
    "clust_file = open(\"../Datasets/Sinanet/Sinanet-master/clusters.txt\",\"r\")\n",
    "clust_iter = 0\n",
    "check = 0\n",
    "for line in tqdm_notebook(clust_file, total=len(G.nodes()), leave=False):\n",
    "    labels = np.asarray(list(map(float,line.split())))\n",
    "    for l in labels:\n",
    "        if l == 0:\n",
    "            if check == 0:\n",
    "                clust_iter += 1\n",
    "                check = 1\n",
    "        else:\n",
    "            if check == 1:\n",
    "                check = 0\n",
    "            label_dict[str(int(l))] = clust_iter\n",
    "clust_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Political Blogs dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = nx.read_gml('../Datasets/Political Blogs/polblogs.gml').to_undirected()\n",
    "\n",
    "polit_vect = {}\n",
    "for key, value in dict(G.nodes(data=True)).items():\n",
    "    appending = 0\n",
    "    if int(value['value']) == 0:\n",
    "        appending = 1\n",
    "    polit_vect.update({key: np.asarray([int(value['value']), appending])})\n",
    "    \n",
    "vect = polit_vect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### WebKB Wisc dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=230.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = nx.read_edgelist('../Datasets/WebKB/Washington.cites.txt').to_undirected()\n",
    "\n",
    "label_dict = dict()\n",
    "vect = {}\n",
    "sim_file = open(\"../Datasets/WebKB/Washington.content.txt\",\"r\")\n",
    "for line in tqdm_notebook(sim_file, total=len(G.nodes()), leave=False):\n",
    "    node = line.split()[0]\n",
    "    label = line.split()[-1]\n",
    "    features = np.asarray(list(map(int,line.split()[1:-2])))\n",
    "    label_dict[node] = label\n",
    "    vect.update({str(node):features})\n",
    "sim_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 1000\n",
    "tau1 = 3\n",
    "tau2 = 2\n",
    "mu = 0.1\n",
    "G_first = nx.generators.community.LFR_benchmark_graph(\n",
    "    n, tau1, tau2, mu, average_degree=100, max_degree=450, seed=11\n",
    ")\n",
    "mu = 0.4\n",
    "G_second = nx.generators.community.LFR_benchmark_graph(\n",
    "    n, tau1, tau2, mu, average_degree=150, max_degree=450, seed=15\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = {frozenset(G_first.nodes[v]['community']) for v in G_first}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "for node_1 in G_second.nodes():\n",
    "    for node_2 in G_second.nodes():\n",
    "        if not G_second.has_edge(node_1,node_2) and G_first.has_edge(node_1,node_2) and G_first.nodes[node_1]['community'] == G_first.nodes[node_2]['community']:\n",
    "            G_first.remove_edge(int(node_1), int(node_2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.empty_graph(3000)\n",
    "G_second = nx.empty_graph(3000)\n",
    "\n",
    "counter = 0\n",
    "for edge in G_first.edges():\n",
    "    if counter % 2 == 0:\n",
    "        G_first.add_edge(edge[0], edge[1])\n",
    "    else:\n",
    "        G_second.add_edge(edge[0], edge[1])\n",
    "    counter += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_vect = {}\n",
    "for i in range(n):\n",
    "    features = np.random.randint(2, size=30)\n",
    "    random_vect.update({int(i):features})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = 0.7\n",
    "tau1 = 3\n",
    "tau2 = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_third = nx.generators.community.LFR_benchmark_graph(\n",
    "    n, tau1, tau2, mu, average_degree=10, max_degree=150, seed=15\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 1000, Number of edges - 12907\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G.number_of_nodes(), G.number_of_edges()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synthetic networks\n",
    "\n",
    "### ER-BA networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.barabasi_albert_graph(500, 13, seed=3)\n",
    "G_second = nx.gnp_random_graph(500, 0.05, seed=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 500, Number of edges - 6331\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G_first.number_of_nodes(), G_first.number_of_edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 500, Number of edges - 24886\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G_second.number_of_nodes(), G_second.number_of_edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.gnp_random_graph(3000, 0.1, seed=10)\n",
    "G_second = nx.gnp_random_graph(3000, 0.4, seed=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ER-Star graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "G_first = nx.gnp_random_graph(3000, 0.1, seed=3)\n",
    "G_second = nx.star_graph(2999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_seq = [i+1 for i in range(100)]\n",
    "fin_first_seq = []\n",
    "fin_second_seq = []\n",
    "for i in range(100):\n",
    "    if i < 50:\n",
    "        fin_first_seq.append(first_seq[i])\n",
    "    else:\n",
    "        fin_first_seq.append(first_seq[i]-1)\n",
    "    \n",
    "fin_second_seq = list(reversed(fin_first_seq))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.generators.degree_seq.havel_hakimi_graph(fin_first_seq)\n",
    "G_second = nx.generators.degree_seq.havel_hakimi_graph(fin_second_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.path_graph(100)\n",
    "G_second = nx.gnp_random_graph(100, 0.3, seed=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_1 = nx.complete_graph(50)\n",
    "G_2 = nx.relabel_nodes(nx.complete_graph(50), lambda x: x + 50)\n",
    "G_3 = nx.relabel_nodes(nx.complete_graph(50), lambda x: x + 100)\n",
    "\n",
    "G_first = nx.union_all([G_1,G_2,G_3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_1 = nx.classic.cycle_graph(50)\n",
    "G_2 = nx.relabel_nodes(nx.cycle_graph(50), lambda x: x + 50)\n",
    "G_3 = nx.relabel_nodes(nx.cycle_graph(50), lambda x: x + 100)\n",
    "\n",
    "G_second = nx.union_all([G_1,G_2,G_3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Edge swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_first = nx.gnp_random_graph(500, 0.05, seed=3)\n",
    "G_second = nx.double_edge_swap(G_first.copy(), nswap = 800, max_tries = 24000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 500, Number of edges - 6235\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G_first.number_of_nodes(), G_first.number_of_edges()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing nodes without features**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 1000, Number of edges - 5521\n"
     ]
    }
   ],
   "source": [
    "nodes = list(G.nodes)\n",
    "\n",
    "for node in nodes:\n",
    "    if node not in vect:\n",
    "        G.remove_node(node)\n",
    "        \n",
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G.number_of_nodes(), G.number_of_edges()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing nodes without edges**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 1000, Number of edges - 5521\n"
     ]
    }
   ],
   "source": [
    "nodes = list(G.nodes)\n",
    "for node in nodes:\n",
    "    degree = G.degree(node)\n",
    "    if degree == 0:\n",
    "        G.remove_node(node)\n",
    "        \n",
    "print('Number of nodes - {0}, Number of edges - {1}'.format(G.number_of_nodes(), G.number_of_edges()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating significance normalization & common interests statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.481289072818526, 1.0753005943180265)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "significance_normalized_vect, omega = significance_normalization(vect)\n",
    "\n",
    "_, mu, sigma = calc_common_interests_stats(G_first, vect)\n",
    "mu, sigma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted graphs for synthetic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is weighted - True\n",
      "Graph is weighted - True\n"
     ]
    }
   ],
   "source": [
    "for e in G_first.edges():\n",
    "    G_first[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(G_first)))\n",
    "\n",
    "\n",
    "for e in G_second.edges():\n",
    "    G_second[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(G_second)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_weight = G_first.size(weight='weight')\n",
    "\n",
    "for e in G_first.edges():\n",
    "    G_first[e[0]][e[1]]['weight'] = 1/f_weight\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is weighted - True\n",
      "Graph is weighted - True\n",
      "Graph is weighted - True\n"
     ]
    }
   ],
   "source": [
    "for e in G_first.edges():\n",
    "    G_first[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(G_first)))\n",
    "\n",
    "for e in G_second.edges():\n",
    "    G_second[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(G_second)))\n",
    "\n",
    "for e in G_third.edges():\n",
    "    G_third[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(G_third)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G_second_f = create_weighted_graph_from_two_graphs(G_second, G_third, alpha=0.45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating weighted graph**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Graph is weighted - True\n"
     ]
    }
   ],
   "source": [
    "modified_G = G.copy()\n",
    "\n",
    "for e in modified_G.edges():\n",
    "    modified_G[e[0]][e[1]]['weight'] = 1\n",
    "    \n",
    "print('Graph is weighted - {0}'.format(nx.is_weighted(modified_G)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If topology is not fixed and similarity matrix file doesn't exist, then calculating similarity matrix and saving it in the file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "fixed_topology = False\n",
    "similarity_file_exists = False\n",
    "\n",
    "similarity_matrix_file_name = 'similarity_matrix_finals_corr_leid_1.txt'\n",
    "if (fixed_topology == False and similarity_file_exists == False):\n",
    "    not_fixed_topology_graph(G_first, vect, omega, cosine_sim, similarity_matrix_file_name)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Constructing graph from the vector data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "attr_G = nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import shuffle\n",
    "\n",
    "sim_file = open(similarity_matrix_file_name,\"r\")\n",
    "for line in tqdm_notebook(sim_file, total=len(G.nodes()), leave=False):\n",
    "    sim_tuple = eval(line)\n",
    "    node = sim_tuple[0]\n",
    "    res_tuple = sim_tuple[1][1:]\n",
    "    for i in range(len(G.nodes())-1):\n",
    "        if node != res_tuple[i][0]:\n",
    "            if not attr_G.has_edge(node, res_tuple[i][0]):\n",
    "                attr_G.add_edge(node, res_tuple[i][0], weight=res_tuple[i][1])\n",
    "sim_file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes - 3000, Number of edges - 4498500\n"
     ]
    }
   ],
   "source": [
    "print('Number of nodes - {0}, Number of edges - {1}'.format(attr_G.number_of_nodes(), attr_G.number_of_edges()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77402.66685016255"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr_G.size(weight='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72667540"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attr_G.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Detecting communities and metrics calculation, based on input parameters**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas =  np.arange(0.0,1.05,0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": true
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      "1000\n",
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      "text/plain": [
       "HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=19388.0), HTML(value='')))"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kek\n",
      "NX Write Done in 0.6782245635986328\n",
      "IG Read Done in 0.5425100326538086\n",
      "Done\n",
      "Leiden Finished in 0.5213234424591064\n",
      "1000\n",
      "Leiden Finished in 0.23636746406555176\n",
      "1000\n",
      "Leiden Finished in 0.3276989459991455\n",
      "1000\n",
      "Leiden Finished in 0.3829813003540039\n",
      "1000\n",
      "Leiden Finished in 0.31345438957214355\n",
      "1000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "similarity_metrics = [cosine_sim]\n",
    "metric_names = ['cosine_sim']\n",
    "gaussian_weighting = [False]\n",
    "algorithm = ['leiden']\n",
    "metrics_report = []\n",
    "clusters = 0\n",
    "partition = []\n",
    "clusts = {}\n",
    "for n, similarity in enumerate(tqdm_notebook(similarity_metrics)):\n",
    "    for alpha in tqdm_notebook(alphas, leave=False):\n",
    "        for k, gaussian in enumerate(tqdm_notebook(gaussian_weighting, leave=False)):\n",
    "            for algo in algorithm:\n",
    "                fname = 'algorithm-{}||metric_name-{}||alpha-{}||gaussian_weighting-{}'.format(algo, metric_names[n], alpha, gaussian_weighting[k])\n",
    " \n",
    "                report,clusters,G,partition = calculate_metrics_graphs(G_first, G_second, alpha, algo=algo, viz=False, average=5, ground_truth=True, gt_labels=gt_labels)\n",
    "\n",
    "                report['alpha'] = alpha\n",
    "                report['algorithm'] = algo\n",
    "                metrics_report.append(report)\n",
    "                clusts.update({alpha:partition})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "topology = 'f' #f - fixed, nf - not fixed\n",
    "sim = 'cos' #cos - cosine similarity, hamm - hamming similarity\n",
    "top_k = '' #kpercent - top k percent (0, 0.25, 0.5, 0.75)\n",
    "dataset = 'cora_dataset' #dataset name\n",
    "result_file_name = dataset+'_'+sim+'_'+topology+top_k+'.txt'\n",
    "hs = open(result_file_name,'w+')\n",
    "hs.write(str(metrics_report))\n",
    "hs.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "hs = open(result_file_name,'r')\n",
    "\n",
    "for line in hs:\n",
    "    metrics_report = eval(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "mods_mean = []\n",
    "mods_std = []\n",
    "entropy_mean = []\n",
    "entropy_std = []\n",
    "attr_mods_mean = []\n",
    "attr_mods_std = []\n",
    "modified_mods_mean = []\n",
    "modified_mods_std = []\n",
    "\n",
    "for i in metrics_report:\n",
    "    mods_mean.append(i['modularity_mean'])\n",
    "    attr_mods_mean.append(i['attr_modularity_mean'])\n",
    "    attr_mods_std.append(i['attr_modularity_std'])\n",
    "    mods_std.append(i['modularity_std'])\n",
    "    modified_mods_mean.append(i['mod_modularity_mean'])\n",
    "    modified_mods_std.append(i['mod_modularity_std'])\n",
    "    entropy_mean.append(1 - i['graph_entropy_mean'])\n",
    "    entropy_std.append(1 - i['graph_entropy_std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Modularities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas =  np.arange(0,1.05,0.05)\n",
    "mods_sum = []\n",
    "mods_alpha = []\n",
    "mods_alpha_std = []\n",
    "attr_mods_alpha = []\n",
    "attr_mods_alpha_std = []\n",
    "mods_mix = []\n",
    "differential_mean = []\n",
    "differential_std = []\n",
    "for i in range(len(mods_mean)):\n",
    "    mods_alpha.append(mods_mean[i] * alphas[i])\n",
    "    mods_alpha_std.append(mods_std[i])\n",
    "    attr_mods_alpha.append(attr_mods_mean[i] * (1-alphas[i]))\n",
    "    attr_mods_alpha_std.append(attr_mods_std[i])    \n",
    "    mods_sum.append(mods_alpha[i] + attr_mods_alpha[i])\n",
    "    if alphas[i] == 0 or alphas[i] == 1:\n",
    "        differential_mean.append(0)\n",
    "        mods_mix.append(0)\n",
    "        differential_std.append(0)\n",
    "    else:\n",
    "        al = (2/(alphas[i] * (1-alphas[i])))\n",
    "        diff_mean = (modified_mods_mean[i] - (alphas[i] * mods_mean[i] + (1-alphas[i]) * attr_mods_mean[i]))\n",
    "        differential_mean.append(diff_mean)\n",
    "        diff_std = math.sqrt(modified_mods_std[i]**2 + (mods_alpha_std[i]**2 + attr_mods_alpha_std[i]**2))\n",
    "        differential_std.append(diff_std)\n",
    "        mix = al * diff_mean\n",
    "        mods_mix.append(mix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ca35b006-973d-4344-9d14-3d7fe83c30c3');\n",
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       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly \n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "trace0 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = modified_mods_mean,\n",
    "    mode = 'lines+markers',\n",
    "    error_y=dict(type='data', array=modified_mods_std),\n",
    "    name = 'Composite',\n",
    "    yaxis='y',\n",
    "    line=dict(color='dodgerblue', width=2,dash='dash')\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = mods_alpha,\n",
    "    mode = 'lines+markers',\n",
    "    error_y=dict(type='data', array=mods_alpha_std),\n",
    "    name = 'Structural',\n",
    "    yaxis='y',\n",
    "    line=dict(color='crimson', width=2)\n",
    ")\n",
    "trace2 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = attr_mods_alpha,\n",
    "    error_y=dict(type='data', array=attr_mods_alpha_std),\n",
    "    mode = 'lines+markers',\n",
    "    name = 'Attributive',\n",
    "    yaxis='y',\n",
    "    line=dict(color='salmon', width=2)\n",
    ")\n",
    "trace3 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = differential_mean,\n",
    "    error_y=dict(type='data', array=differential_std),\n",
    "    mode = 'lines+markers',\n",
    "    name = 'Differential',\n",
    "    yaxis='y',\n",
    "    line=dict(color='darkblue', width=2, dash='dash')\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2, trace3]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title='Modularity'\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        title='NMI',\n",
    "        titlefont=dict(\n",
    "            color='rgb(148, 103, 189)'\n",
    "        ),\n",
    "        tickfont=dict(\n",
    "            color='rgb(148, 103, 189)'\n",
    "        ),\n",
    "        overlaying='y',\n",
    "        side='right'\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=r'$\\alpha$'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.update_layout(legend=dict(x=0, y=-0.2))\n",
    "fig.update_layout(legend_orientation=\"h\")\n",
    "fig.update_layout(showlegend=False)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes-aware Modularity and Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Norm(x):\n",
    "    mi=min(x)\n",
    "    ma=max(np.array(x)-mi)\n",
    "    x_n=(np.array(x)-mi)/ma\n",
    "    return x_n\n",
    "\n",
    "entropy_mean_norm = Norm(entropy_mean)\n",
    "attr_mods_mean_fixed_norm = Norm(attr_mods_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly \n",
    "\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "trace0 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = entropy_mean_norm,\n",
    "    mode = 'lines+markers',\n",
    "    #error_y=dict(type='data', array=mod_mods_std_fixed),\n",
    "    name = 'Min-max normalized Anti-Entropy',\n",
    "    yaxis='y',\n",
    "    line=dict(color='dodgerblue', width=2,dash='dash')\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x = alphas,\n",
    "    y = attr_mods_mean_fixed_norm,\n",
    "    mode = 'lines+markers',\n",
    "    #error_y=dict(type='data', array=mods_alpha_std),\n",
    "    name = 'Min-max normalized Attributes-aware Modularity',\n",
    "    yaxis='y',\n",
    "    line=dict(color='crimson', width=2)\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(\n",
    "        title='Quality measure value'\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        title=r'$\\alpha$'\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "fig.update_layout(legend=dict(x=0, y=-0.2))\n",
    "fig.update_layout(legend_orientation=\"h\")\n",
    "fig.update_layout(showlegend=True)\n",
    "\n",
    "fig.show()\n",
    "fig.write_image(\"leiden_paper/cora_ent_attr.pdf\")\n"
   ]
  }
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