Preprocessing - Analysis - Train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ff448d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from torch_geometric.data import Data\n",
    "from torch_geometric.utils.convert import to_networkx\n",
    "import networkx as nx\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0bdc7e17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_df(path):\n",
    "    # Read the text file line by line\n",
    "    lines = []\n",
    "    with open(path, \"r\") as file:\n",
    "        lines = file.readlines()\n",
    "\n",
    "    # Prepare an empty DataFrame with 10 columns\n",
    "    df = pd.DataFrame(columns=['Video ID', 'uploader', 'age', 'category', 'length', 'views', 'rate', 'ratings', 'comments', 'related IDs'])\n",
    "\n",
    "    # Iterate over the lines and populate the DataFrame\n",
    "    for line in lines:\n",
    "        # Split the line by tabs\n",
    "        values = line.strip().split(\"\\t\")\n",
    "\n",
    "        start = values[:9]\n",
    "        end = values[9:]\n",
    "        start.append(end)\n",
    "        try:\n",
    "            df.loc[len(df)] = start\n",
    "        except:\n",
    "            print(f\"Error for row: {start}\")\n",
    "            \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4c824a66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Error for row: ['RhGj7gIM4DA', []]\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"C:\\\\Users\\\\ANDRE\\\\OneDrive\\\\Desktop\\\\Andreas_Sideras\\\\Demokritos\\\\Msc in AI\\\\2nd Semester\\\\Applications of AI\\\\0518\"\n",
    "path = os.path.join(data_dir, \"0.txt\")\n",
    "df_0 = load_df(path)\n",
    "path = os.path.join(data_dir, \"1.txt\")\n",
    "df_1 = load_df(path)\n",
    "path = os.path.join(data_dir, \"2.txt\")\n",
    "df_2 = load_df(path)\n",
    "# path = os.path.join(data_dir, \"3.txt\")\n",
    "# df_3 = load_df(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f8623ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([df_0, df_1,df_2])\n",
    "data = data.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e548a57",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Video ID</th>\n",
       "      <th>uploader</th>\n",
       "      <th>age</th>\n",
       "      <th>category</th>\n",
       "      <th>length</th>\n",
       "      <th>views</th>\n",
       "      <th>rate</th>\n",
       "      <th>ratings</th>\n",
       "      <th>comments</th>\n",
       "      <th>related IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D6frFp-VwHs</td>\n",
       "      <td>yetube</td>\n",
       "      <td>821</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>30</td>\n",
       "      <td>554455</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2813</td>\n",
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       "      <td>0Lg4i2C6zws</td>\n",
       "      <td>TNAwrestling</td>\n",
       "      <td>821</td>\n",
       "      <td>Sports</td>\n",
       "      <td>573</td>\n",
       "      <td>191461</td>\n",
       "      <td>4.46</td>\n",
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       "      <td>111</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UJpgxqYGws4</td>\n",
       "      <td>jrc0803</td>\n",
       "      <td>820</td>\n",
       "      <td>Sports</td>\n",
       "      <td>55</td>\n",
       "      <td>160852</td>\n",
       "      <td>4.09</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BAPwg5nCKxE</td>\n",
       "      <td>milanoss</td>\n",
       "      <td>820</td>\n",
       "      <td>Film &amp; Animation</td>\n",
       "      <td>578</td>\n",
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       "      <th>4</th>\n",
       "      <td>vVJ06ixj19Q</td>\n",
       "      <td>PimpimusPrime</td>\n",
       "      <td>820</td>\n",
       "      <td>Film &amp; Animation</td>\n",
       "      <td>29</td>\n",
       "      <td>95950</td>\n",
       "      <td>4.61</td>\n",
       "      <td>134</td>\n",
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       "    <tr>\n",
       "      <th>9075</th>\n",
       "      <td>jJegAHMIsf4</td>\n",
       "      <td>alertgoo</td>\n",
       "      <td>787</td>\n",
       "      <td>News &amp; Politics</td>\n",
       "      <td>163</td>\n",
       "      <td>700</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[u70TErl9EW0, bqgDlv0QXbs, F9S8EM37Fo4, DQRVFI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9076</th>\n",
       "      <td>TOwAygRwJFs</td>\n",
       "      <td>telo003</td>\n",
       "      <td>629</td>\n",
       "      <td>Music</td>\n",
       "      <td>254</td>\n",
       "      <td>1005</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[a-gCCDkCfFs, UiKAO262e6g, 1K_3Ey1ANLQ, GYfaaf...</td>\n",
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       "    <tr>\n",
       "      <th>9077</th>\n",
       "      <td>DrPbTvUDhPg</td>\n",
       "      <td>tripodfilmsllc</td>\n",
       "      <td>475</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>577</td>\n",
       "      <td>595</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[Qo1gUoA6p1U, b2f4heaG288, IUL61LYOUiA, RdIjU5...</td>\n",
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       "      <td>ivisonrj</td>\n",
       "      <td>576</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>36</td>\n",
       "      <td>3094</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
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       "      <td>TvXZvu1Ppok</td>\n",
       "      <td>tripodfilmsllc</td>\n",
       "      <td>474</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>240</td>\n",
       "      <td>307</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "         Video ID        uploader  age          category length   views  rate  \\\n",
       "0     D6frFp-VwHs          yetube  821     Entertainment     30  554455  3.54   \n",
       "1     0Lg4i2C6zws    TNAwrestling  821            Sports    573  191461  4.46   \n",
       "2     UJpgxqYGws4         jrc0803  820            Sports     55  160852  4.09   \n",
       "3     BAPwg5nCKxE        milanoss  820  Film & Animation    578  170536  4.06   \n",
       "4     vVJ06ixj19Q   PimpimusPrime  820  Film & Animation     29   95950  4.61   \n",
       "...           ...             ...  ...               ...    ...     ...   ...   \n",
       "9075  jJegAHMIsf4        alertgoo  787   News & Politics    163     700   4.2   \n",
       "9076  TOwAygRwJFs         telo003  629             Music    254    1005   4.5   \n",
       "9077  DrPbTvUDhPg  tripodfilmsllc  475            Comedy    577     595  4.33   \n",
       "9078  fCFD-GfvK7Q        ivisonrj  576    People & Blogs     36    3094   4.2   \n",
       "9079  TvXZvu1Ppok  tripodfilmsllc  474            Comedy    240     307  4.33   \n",
       "\n",
       "     ratings comments                                        related IDs  \n",
       "0       2813      422  [6t8HbzleQqE, dmfq_g3sQHE, D6frFp-VwHs, uc3Z72...  \n",
       "1        217      111  [YRiXuRIDFzY, gUzkrbr_KP0, PsXfjt3wRLU, Iid3aF...  \n",
       "2        486      423  [3KtB_SAOuM4, i65LHf0A0aE, PKPDH2a82dA, vhRFLt...  \n",
       "3         82       91  [QOZSRDYm1SY, UbhEunreGwQ, 6OHhe522B1Y, 9sc4as...  \n",
       "4        134      185  [eH7D7Y0cttY, C4vPhkv1wRE, euZcw5OaouE, 9MVXhP...  \n",
       "...      ...      ...                                                ...  \n",
       "9075       5        0  [u70TErl9EW0, bqgDlv0QXbs, F9S8EM37Fo4, DQRVFI...  \n",
       "9076       2        1  [a-gCCDkCfFs, UiKAO262e6g, 1K_3Ey1ANLQ, GYfaaf...  \n",
       "9077       3        1  [Qo1gUoA6p1U, b2f4heaG288, IUL61LYOUiA, RdIjU5...  \n",
       "9078       5       26  [6w7k1jEAKiM, G_tF10OCYbM, EVywb0_E1ME, MwVK_m...  \n",
       "9079       3        1  [DrPbTvUDhPg, SkFAYGepu9w, SLaMNTIYG3M, IJGfmD...  \n",
       "\n",
       "[9080 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0b53abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "category_mapping = {\n",
    "    'Entertainment'    :0,\n",
    "    'Sports'           :1,\n",
    "    'Film & Animation' :2,\n",
    "    'News & Politics'  :3,\n",
    "    'Autos & Vehicles' :4,\n",
    "    'Music'            :5,\n",
    "    'People & Blogs'   :6,\n",
    "    ' UNA '            :7,\n",
    "    'Comedy'           :8,\n",
    "    'Gadgets & Games'  :9,\n",
    "    'Pets & Animals'   :10,\n",
    "    'Howto & DIY'      :11,\n",
    "    'Travel & Places'  :12\n",
    "}\n",
    "\n",
    "data['category'] = data['category'].map(category_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cfa3831",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Video ID</th>\n",
       "      <th>uploader</th>\n",
       "      <th>age</th>\n",
       "      <th>category</th>\n",
       "      <th>length</th>\n",
       "      <th>views</th>\n",
       "      <th>rate</th>\n",
       "      <th>ratings</th>\n",
       "      <th>comments</th>\n",
       "      <th>related IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D6frFp-VwHs</td>\n",
       "      <td>yetube</td>\n",
       "      <td>821</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>554455</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2813</td>\n",
       "      <td>422</td>\n",
       "      <td>[6t8HbzleQqE, dmfq_g3sQHE, D6frFp-VwHs, uc3Z72...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0Lg4i2C6zws</td>\n",
       "      <td>TNAwrestling</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>573</td>\n",
       "      <td>191461</td>\n",
       "      <td>4.46</td>\n",
       "      <td>217</td>\n",
       "      <td>111</td>\n",
       "      <td>[YRiXuRIDFzY, gUzkrbr_KP0, PsXfjt3wRLU, Iid3aF...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UJpgxqYGws4</td>\n",
       "      <td>jrc0803</td>\n",
       "      <td>820</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>160852</td>\n",
       "      <td>4.09</td>\n",
       "      <td>486</td>\n",
       "      <td>423</td>\n",
       "      <td>[3KtB_SAOuM4, i65LHf0A0aE, PKPDH2a82dA, vhRFLt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BAPwg5nCKxE</td>\n",
       "      <td>milanoss</td>\n",
       "      <td>820</td>\n",
       "      <td>2</td>\n",
       "      <td>578</td>\n",
       "      <td>170536</td>\n",
       "      <td>4.06</td>\n",
       "      <td>82</td>\n",
       "      <td>91</td>\n",
       "      <td>[QOZSRDYm1SY, UbhEunreGwQ, 6OHhe522B1Y, 9sc4as...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>vVJ06ixj19Q</td>\n",
       "      <td>PimpimusPrime</td>\n",
       "      <td>820</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>95950</td>\n",
       "      <td>4.61</td>\n",
       "      <td>134</td>\n",
       "      <td>185</td>\n",
       "      <td>[eH7D7Y0cttY, C4vPhkv1wRE, euZcw5OaouE, 9MVXhP...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Video ID       uploader  age  category length   views  rate ratings  \\\n",
       "0  D6frFp-VwHs         yetube  821         0     30  554455  3.54    2813   \n",
       "1  0Lg4i2C6zws   TNAwrestling  821         1    573  191461  4.46     217   \n",
       "2  UJpgxqYGws4        jrc0803  820         1     55  160852  4.09     486   \n",
       "3  BAPwg5nCKxE       milanoss  820         2    578  170536  4.06      82   \n",
       "4  vVJ06ixj19Q  PimpimusPrime  820         2     29   95950  4.61     134   \n",
       "\n",
       "  comments                                        related IDs  \n",
       "0      422  [6t8HbzleQqE, dmfq_g3sQHE, D6frFp-VwHs, uc3Z72...  \n",
       "1      111  [YRiXuRIDFzY, gUzkrbr_KP0, PsXfjt3wRLU, Iid3aF...  \n",
       "2      423  [3KtB_SAOuM4, i65LHf0A0aE, PKPDH2a82dA, vhRFLt...  \n",
       "3       91  [QOZSRDYm1SY, UbhEunreGwQ, 6OHhe522B1Y, 9sc4as...  \n",
       "4      185  [eH7D7Y0cttY, C4vPhkv1wRE, euZcw5OaouE, 9MVXhP...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04a76bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "node_IDs = set(data['Video ID'].unique())\n",
    "\n",
    "#drop the links that link to nodes that I do not have info about\n",
    "data['related IDs'] = data['related IDs'].apply(lambda x: [item for item in x if item in node_IDs] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7067d158",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "      <th>Video ID</th>\n",
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       "      <td>4.5</td>\n",
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       "      <td>[R9oSd_d3KZU]</td>\n",
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      ],
      "text/plain": [
       "         Video ID        uploader  age  category length   views  rate ratings  \\\n",
       "0     D6frFp-VwHs          yetube  821         0     30  554455  3.54    2813   \n",
       "1     0Lg4i2C6zws    TNAwrestling  821         1    573  191461  4.46     217   \n",
       "2     UJpgxqYGws4         jrc0803  820         1     55  160852  4.09     486   \n",
       "3     BAPwg5nCKxE        milanoss  820         2    578  170536  4.06      82   \n",
       "4     vVJ06ixj19Q   PimpimusPrime  820         2     29   95950  4.61     134   \n",
       "...           ...             ...  ...       ...    ...     ...   ...     ...   \n",
       "9075  jJegAHMIsf4        alertgoo  787         3    163     700   4.2       5   \n",
       "9076  TOwAygRwJFs         telo003  629         5    254    1005   4.5       2   \n",
       "9077  DrPbTvUDhPg  tripodfilmsllc  475         8    577     595  4.33       3   \n",
       "9078  fCFD-GfvK7Q        ivisonrj  576         6     36    3094   4.2       5   \n",
       "9079  TvXZvu1Ppok  tripodfilmsllc  474         8    240     307  4.33       3   \n",
       "\n",
       "     comments                                        related IDs  \n",
       "0         422  [6t8HbzleQqE, dmfq_g3sQHE, D6frFp-VwHs, uc3Z72...  \n",
       "1         111            [9waJ0vOtpsY, x9CMW1URsEo, PiGDwZPgRLM]  \n",
       "2         423  [3KtB_SAOuM4, i65LHf0A0aE, PKPDH2a82dA, vhRFLt...  \n",
       "3          91  [UbhEunreGwQ, 6OHhe522B1Y, 9sc4asoixhk, swttQ7...  \n",
       "4         185  [F5qsdZFcrKM, vJNrJX4ysmA, 4eaDX8tzqj4, fdAF1c...  \n",
       "...       ...                                                ...  \n",
       "9075        0                                                 []  \n",
       "9076        1                                      [R9oSd_d3KZU]  \n",
       "9077        1                                                 []  \n",
       "9078       26                                                 []  \n",
       "9079        1                                      [DrPbTvUDhPg]  \n",
       "\n",
       "[9080 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4836bd8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I found 16 disconnected nodes\n"
     ]
    }
   ],
   "source": [
    "#find disconnected nodes and remove them\n",
    "leafes = data[data['related IDs'].apply(len) == 0]\n",
    "leafes_ids = set(leafes['Video ID'])\n",
    "for _, ids in data['related IDs'].items():\n",
    "    for node in ids:\n",
    "        if node in leafes_ids:\n",
    "            leafes_ids.remove(node)\n",
    "print(f'I found {len(leafes_ids)} disconnected nodes')\n",
    "graph = data[~data['Video ID'].isin(leafes_ids)]\n",
    "graph = graph.drop('uploader',axis = 1)\n",
    "graph = graph.rename(columns={'Video ID': 'node', 'related IDs':'links to'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "154a1494",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = graph.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c277326a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>[R9oSd_d3KZU]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9061</th>\n",
       "      <td>DrPbTvUDhPg</td>\n",
       "      <td>475</td>\n",
       "      <td>8</td>\n",
       "      <td>577</td>\n",
       "      <td>595</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9062</th>\n",
       "      <td>fCFD-GfvK7Q</td>\n",
       "      <td>576</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>3094</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9063</th>\n",
       "      <td>TvXZvu1Ppok</td>\n",
       "      <td>474</td>\n",
       "      <td>8</td>\n",
       "      <td>240</td>\n",
       "      <td>307</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[DrPbTvUDhPg]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9064 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             node  age  category length   views  rate ratings comments  \\\n",
       "0     D6frFp-VwHs  821         0     30  554455  3.54    2813      422   \n",
       "1     0Lg4i2C6zws  821         1    573  191461  4.46     217      111   \n",
       "2     UJpgxqYGws4  820         1     55  160852  4.09     486      423   \n",
       "3     BAPwg5nCKxE  820         2    578  170536  4.06      82       91   \n",
       "4     vVJ06ixj19Q  820         2     29   95950  4.61     134      185   \n",
       "...           ...  ...       ...    ...     ...   ...     ...      ...   \n",
       "9059  jJegAHMIsf4  787         3    163     700   4.2       5        0   \n",
       "9060  TOwAygRwJFs  629         5    254    1005   4.5       2        1   \n",
       "9061  DrPbTvUDhPg  475         8    577     595  4.33       3        1   \n",
       "9062  fCFD-GfvK7Q  576         6     36    3094   4.2       5       26   \n",
       "9063  TvXZvu1Ppok  474         8    240     307  4.33       3        1   \n",
       "\n",
       "                                               links to  \n",
       "0     [6t8HbzleQqE, dmfq_g3sQHE, D6frFp-VwHs, uc3Z72...  \n",
       "1               [9waJ0vOtpsY, x9CMW1URsEo, PiGDwZPgRLM]  \n",
       "2     [3KtB_SAOuM4, i65LHf0A0aE, PKPDH2a82dA, vhRFLt...  \n",
       "3     [UbhEunreGwQ, 6OHhe522B1Y, 9sc4asoixhk, swttQ7...  \n",
       "4     [F5qsdZFcrKM, vJNrJX4ysmA, 4eaDX8tzqj4, fdAF1c...  \n",
       "...                                                 ...  \n",
       "9059                                                 []  \n",
       "9060                                      [R9oSd_d3KZU]  \n",
       "9061                                                 []  \n",
       "9062                                                 []  \n",
       "9063                                      [DrPbTvUDhPg]  \n",
       "\n",
       "[9064 rows x 9 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "abcb5f5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create an index that maps Video IDs to indexes. We'll need it for later processing\n",
    "node_ID_index = { video_id : idx for idx, video_id in graph['node'].items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "145b71ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9064"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(node_ID_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e6890a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph['node'] = graph['node'].map(node_ID_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "404c8785",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph['links to'] = graph['links to'].apply(lambda x: [node_ID_index[item] for item in x] )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2bb1f1d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node</th>\n",
       "      <th>age</th>\n",
       "      <th>category</th>\n",
       "      <th>length</th>\n",
       "      <th>views</th>\n",
       "      <th>rate</th>\n",
       "      <th>ratings</th>\n",
       "      <th>comments</th>\n",
       "      <th>links to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>821</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>554455</td>\n",
       "      <td>3.54</td>\n",
       "      <td>2813</td>\n",
       "      <td>422</td>\n",
       "      <td>[243, 244, 0, 245, 246, 247, 248, 249, 250]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>821</td>\n",
       "      <td>1</td>\n",
       "      <td>573</td>\n",
       "      <td>191461</td>\n",
       "      <td>4.46</td>\n",
       "      <td>217</td>\n",
       "      <td>111</td>\n",
       "      <td>[251, 252, 253]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>820</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>160852</td>\n",
       "      <td>4.09</td>\n",
       "      <td>486</td>\n",
       "      <td>423</td>\n",
       "      <td>[254, 255, 256, 257, 258, 259, 260, 261, 262]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>820</td>\n",
       "      <td>2</td>\n",
       "      <td>578</td>\n",
       "      <td>170536</td>\n",
       "      <td>4.06</td>\n",
       "      <td>82</td>\n",
       "      <td>91</td>\n",
       "      <td>[5, 263, 264, 265, 266, 267, 268, 269, 270, 27...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>820</td>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>95950</td>\n",
       "      <td>4.61</td>\n",
       "      <td>134</td>\n",
       "      <td>185</td>\n",
       "      <td>[276, 277, 278, 279, 280, 281, 282, 283]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9059</th>\n",
       "      <td>9059</td>\n",
       "      <td>787</td>\n",
       "      <td>3</td>\n",
       "      <td>163</td>\n",
       "      <td>700</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9060</th>\n",
       "      <td>9060</td>\n",
       "      <td>629</td>\n",
       "      <td>5</td>\n",
       "      <td>254</td>\n",
       "      <td>1005</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>[9057]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9061</th>\n",
       "      <td>9061</td>\n",
       "      <td>475</td>\n",
       "      <td>8</td>\n",
       "      <td>577</td>\n",
       "      <td>595</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9062</th>\n",
       "      <td>9062</td>\n",
       "      <td>576</td>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "      <td>3094</td>\n",
       "      <td>4.2</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9063</th>\n",
       "      <td>9063</td>\n",
       "      <td>474</td>\n",
       "      <td>8</td>\n",
       "      <td>240</td>\n",
       "      <td>307</td>\n",
       "      <td>4.33</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>[9061]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9064 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      node  age  category length   views  rate ratings comments  \\\n",
       "0        0  821         0     30  554455  3.54    2813      422   \n",
       "1        1  821         1    573  191461  4.46     217      111   \n",
       "2        2  820         1     55  160852  4.09     486      423   \n",
       "3        3  820         2    578  170536  4.06      82       91   \n",
       "4        4  820         2     29   95950  4.61     134      185   \n",
       "...    ...  ...       ...    ...     ...   ...     ...      ...   \n",
       "9059  9059  787         3    163     700   4.2       5        0   \n",
       "9060  9060  629         5    254    1005   4.5       2        1   \n",
       "9061  9061  475         8    577     595  4.33       3        1   \n",
       "9062  9062  576         6     36    3094   4.2       5       26   \n",
       "9063  9063  474         8    240     307  4.33       3        1   \n",
       "\n",
       "                                               links to  \n",
       "0           [243, 244, 0, 245, 246, 247, 248, 249, 250]  \n",
       "1                                       [251, 252, 253]  \n",
       "2         [254, 255, 256, 257, 258, 259, 260, 261, 262]  \n",
       "3     [5, 263, 264, 265, 266, 267, 268, 269, 270, 27...  \n",
       "4              [276, 277, 278, 279, 280, 281, 282, 283]  \n",
       "...                                                 ...  \n",
       "9059                                                 []  \n",
       "9060                                             [9057]  \n",
       "9061                                                 []  \n",
       "9062                                                 []  \n",
       "9063                                             [9061]  \n",
       "\n",
       "[9064 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8827040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([9064, 7])\n"
     ]
    }
   ],
   "source": [
    "features_columns = ['age', 'category', 'length','views', 'rate' , 'ratings', 'comments']\n",
    "node_features_np = graph[features_columns].astype(float).values\n",
    "node_features_tensor = torch.tensor(node_features_np)\n",
    "print(node_features_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d28ee49b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 9064 nodes\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {node_features_tensor.shape[0]} nodes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a599401a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 43963 edges\n"
     ]
    }
   ],
   "source": [
    "edges = torch.empty(2,0)\n",
    "\n",
    "for index, row in graph.iterrows():\n",
    "\n",
    "    node = row['node']\n",
    "    links = row['links to']\n",
    "    tensor = torch.tensor([ [node] * len(links), links])\n",
    "    edges = torch.cat((edges,tensor), dim = 1)\n",
    "edges = edges.int().to(torch.int64)\n",
    "print(f\"There are {edges.shape[1]} edges\")\n",
    "\n",
    "graph = Data(x=node_features_tensor, edge_index=edges)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cc432c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[9064, 7], edge_index=[2, 43963])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de5a4888",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the Data object\n",
    "torch.save(graph, 'MEDIUMgraph.pt')\n",
    "#graph = torch.load('MEDIUMgraph.pt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33c81551",
   "metadata": {},
   "source": [
    "# Graph Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3f2147",
   "metadata": {},
   "source": [
    "Convert PyTorch Geometric object to networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "54db65c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "graph = torch.load('MEDIUMgraph.pt')\n",
    "graphX = to_networkx(graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "006519c2",
   "metadata": {},
   "source": [
    "In a directed graph, a strongly connected component (SCC) is a subset of nodes where there is a directed path from any node in the subset to any other node in the subset. In simpler terms, it represents a group of nodes in which every node is reachable from every other node within the subset. We plot it for demonstration reasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f4f7c1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "strong_components = list(nx.strongly_connected_components(graphX))\n",
    "# Select the first strongly connected component with more than one node\n",
    "selected_component = [comp for comp in strong_components if len(comp) > 10][0]\n",
    "# Create a subgraph using the selected component\n",
    "subgraph = graphX.subgraph(selected_component)\n",
    "fig, ax = plt.subplots()\n",
    "pos = nx.spring_layout(subgraph)\n",
    "nx.draw(subgraph, pos, with_labels=True, node_color='lightblue', node_size=200, font_size=8, ax=ax)\n",
    "plt.title(\"Connected Subgraph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cad16fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# Calculate the in-degree and out-degree of each node in the graph\n",
    "in_degrees = dict(graphX.in_degree())\n",
    "out_degrees = dict(graphX.out_degree())\n",
    "\n",
    "# Calculate the in-degree distribution\n",
    "in_degree_distribution = list(Counter(in_degrees.values()).values())\n",
    "\n",
    "# Calculate the out-degree distribution\n",
    "out_degree_distribution = list(Counter(out_degrees.values()).values())\n",
    "\n",
    "# Define a colormap with warm colors\n",
    "cmap = plt.get_cmap(\"YlOrRd\")\n",
    "\n",
    "# Normalize the values to range between 0 and 1\n",
    "norm_in = plt.Normalize(min(in_degree_distribution), max(in_degree_distribution))\n",
    "norm_out = plt.Normalize(min(out_degree_distribution), max(out_degree_distribution))\n",
    "\n",
    "# Create a list of colors based on the normalized values for in-degree and out-degree\n",
    "colors_in = [cmap(norm_in(value)) for value in in_degree_distribution]\n",
    "colors_out = [cmap(norm_out(value)) for value in out_degree_distribution]\n",
    "\n",
    "# Plot in-degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(in_degree_distribution)), in_degree_distribution, color=colors_in)\n",
    "plt.xlabel(\"In-degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"In-degree Distribution\")\n",
    "plt.show()\n",
    "\n",
    "# Plot out-degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(out_degree_distribution)), out_degree_distribution, color=colors_out)\n",
    "plt.xlabel(\"Out-degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Out-degree Distribution\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "undirected_graph = graphX.to_undirected()\n",
    "\n",
    "# Calculate the combined degree (in-degree + out-degree) of each node in the graph\n",
    "combined_degrees = dict(undirected_graph.degree())\n",
    "\n",
    "# Calculate the undirected degree distribution\n",
    "undirected_degree_distribution = list(Counter(combined_degrees.values()).values())\n",
    "\n",
    "# Plot combined degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(undirected_degree_distribution)), undirected_degree_distribution)\n",
    "plt.xlabel(\"Combined Degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Combined Degree Distribution\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d751d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "\n",
    "# Calculate the in-degree and out-degree of each node in the graph\n",
    "in_degrees = dict(graphX.in_degree())\n",
    "out_degrees = dict(graphX.out_degree())\n",
    "\n",
    "# Calculate the in-degree distribution\n",
    "in_degree_distribution = list(Counter(in_degrees.values()).values())\n",
    "\n",
    "# Calculate the out-degree distribution\n",
    "out_degree_distribution = list(Counter(out_degrees.values()).values())\n",
    "\n",
    "# Define a colormap with warm colors\n",
    "cmap = plt.get_cmap(\"BuPu\")\n",
    "\n",
    "# Normalize the values to range between 0 and 1\n",
    "norm_in = plt.Normalize(min(in_degree_distribution), max(in_degree_distribution))\n",
    "norm_out = plt.Normalize(min(out_degree_distribution), max(out_degree_distribution))\n",
    "\n",
    "# Create a list of colors based on the normalized values for in-degree and out-degree\n",
    "colors_in = [cmap(norm_in(value)) for value in in_degree_distribution]\n",
    "colors_out = [cmap(norm_out(value)) for value in out_degree_distribution]\n",
    "\n",
    "# Plot in-degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(in_degree_distribution)), in_degree_distribution, color=colors_in)\n",
    "plt.xlabel(\"In-degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"In-degree Distribution (Log Scale)\")\n",
    "plt.yscale('log')  # Convert y-axis to log scale\n",
    "plt.show()\n",
    "\n",
    "# Plot out-degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(out_degree_distribution)), out_degree_distribution, color=colors_out)\n",
    "plt.xlabel(\"Out-degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Out-degree Distribution (Log Scale)\")\n",
    "plt.yscale('log')  # Convert y-axis to log scale\n",
    "plt.show()\n",
    "\n",
    "\n",
    "undirected_graph = graphX.to_undirected()\n",
    "\n",
    "# Calculate the combined degree (in-degree + out-degree) of each node in the graph\n",
    "combined_degrees = dict(undirected_graph.degree())\n",
    "\n",
    "# Calculate the undirected degree distribution\n",
    "undirected_degree_distribution = list(Counter(combined_degrees.values()).values())\n",
    "\n",
    "# Plot combined degree distribution\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.bar(range(len(undirected_degree_distribution)), undirected_degree_distribution)\n",
    "plt.xlabel(\"Combined Degree\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Combined Degree Distribution (Log Scale)\")\n",
    "plt.yscale('log')  # Convert y-axis to log scale\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "424984f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "lengths = []\n",
    "\n",
    "# Compute all-pairs shortest paths\n",
    "all_shortest_paths = nx.all_pairs_shortest_path(graphX)\n",
    "\n",
    "# Iterate over the shortest paths\n",
    "for source, shortest_paths in all_shortest_paths:\n",
    "    for target, path in shortest_paths.items():\n",
    "        path_length = len(path)\n",
    "        lengths.append(path_length)\n",
    "counter = Counter(lengths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "94108223",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANDRE\\AppData\\Local\\Temp\\ipykernel_2072\\881317986.py:9: MatplotlibDeprecationWarning: Unable to determine Axes to steal space for Colorbar. Using gca(), but will raise in the future. Either provide the *cax* argument to use as the Axes for the Colorbar, provide the *ax* argument to steal space from it, or add *mappable* to an Axes.\n",
      "  plt.colorbar(sm)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = list(counter.values())\n",
    "\n",
    "cmap = plt.get_cmap(\"YlOrRd\")\n",
    "norm = plt.Normalize(min(values), max(values))\n",
    "colors = [cmap(norm(value)) for value in values]\n",
    "plt.bar(range(len(values)), values, color=colors)\n",
    "sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n",
    "sm.set_array([])  \n",
    "plt.colorbar(sm)\n",
    "plt.xlabel(\"Path Length\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Shortest Path Length Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f2876f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# degree centrality\n",
    "degree_centrality = nx.degree_centrality(graphX)\n",
    "\n",
    "# betweenness centrality\n",
    "betweenness_centrality = nx.betweenness_centrality(graphX)\n",
    "\n",
    "# closeness centrality\n",
    "closeness_centrality = nx.closeness_centrality(graphX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "913153c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x1200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Define a colormap with warm colors\n",
    "cmap = plt.get_cmap(\"YlOrRd\")\n",
    "\n",
    "# Create subplots in a 3x1 grid\n",
    "fig, axes = plt.subplots(3, 1, figsize=(6, 12))\n",
    "\n",
    "# Plot histogram for degree centrality\n",
    "degree_values = list(degree_centrality.values())\n",
    "axes[0].hist(degree_values, bins=20, color=cmap(0.9))\n",
    "axes[0].set_xlabel(\"Degree Centrality\")\n",
    "axes[0].set_ylabel(\"Frequency\")\n",
    "axes[0].set_title(\"Degree Centrality Distribution\")\n",
    "\n",
    "# Plot histogram for betweenness centrality\n",
    "betweenness_values = list(betweenness_centrality.values())\n",
    "axes[1].hist(betweenness_values, bins=5, color=cmap(0.9))\n",
    "axes[1].set_xlabel(\"Betweenness Centrality\")\n",
    "axes[1].set_ylabel(\"Frequency\")\n",
    "axes[1].set_title(\"Betweenness Centrality Distribution\")\n",
    "\n",
    "# Plot histogram for closeness centrality\n",
    "closeness_values = list(closeness_centrality.values())\n",
    "axes[2].hist(closeness_values, bins=10, color=cmap(0.9))\n",
    "axes[2].set_xlabel(\"Closeness Centrality\")\n",
    "axes[2].set_ylabel(\"Frequency\")\n",
    "axes[2].set_title(\"Closeness Centrality Distribution\")\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69189d6c",
   "metadata": {},
   "source": [
    "# Link prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8246a394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[9064, 7], edge_index=[2, 43963])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "381ea60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9064, 7])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph.x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "62075613",
   "metadata": {},
   "outputs": [],
   "source": [
    "degree_centrality_feats = torch.tensor((list(degree_centrality.values()))).unsqueeze(1)\n",
    "betweenness_centrality_feats = torch.tensor((list(betweenness_centrality.values()))).unsqueeze(1)\n",
    "closeness_centrality_feats = torch.tensor((list(closeness_centrality.values()))).unsqueeze(1)\n",
    "pagerank_feats = torch.tensor(list(nx.pagerank(graphX).values())).unsqueeze(1)\n",
    "eigenvector_centrality_feats = torch.tensor((list(nx.eigenvector_centrality(graphX).values()))).unsqueeze(1)\n",
    "in_degree_feats = torch.tensor(list(dict(graphX.in_degree()).values())).unsqueeze(1)\n",
    "out_degree_feats = torch.tensor(list(dict(graphX.out_degree()).values())).unsqueeze(1)\n",
    "average_neighbor_degree_feats = torch.tensor(list(nx.average_neighbor_degree(graphX).values())).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "c90380ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_features_x = torch.cat((graph.x,\n",
    "                            degree_centrality_feats,\n",
    "                            betweenness_centrality_feats,\n",
    "                            closeness_centrality_feats,\n",
    "                            pagerank_feats,\n",
    "                            eigenvector_centrality_feats,\n",
    "                            in_degree_feats,\n",
    "                            out_degree_feats,\n",
    "                            average_neighbor_degree_feats\n",
    "                           ), dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "6d8d4021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([9064, 15])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_features_x.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "06e101ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph.x=new_features_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "a6a31273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[9064, 15], edge_index=[2, 43963])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5ce3ee0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(graph, 'ENHANCEDgraph.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e1229b5d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of node features: 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANDRE\\Anaconda3\\envs\\GNNs\\lib\\site-packages\\torch_geometric\\deprecation.py:22: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
      "  warnings.warn(out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size : 80204 edges\n",
      "Validation set size : 2358 edges\n",
      "Test set size : 4716 edges\n",
      "\n",
      "GAE(\n",
      "  (linear1): Linear(in_features=15, out_features=64, bias=True)\n",
      "  (linear2): Linear(in_features=64, out_features=256, bias=True)\n",
      "  (linear3): Linear(in_features=256, out_features=1024, bias=True)\n",
      "  (conv): GCN(1024, 256, num_layers=2)\n",
      "  (linear4): Linear(in_features=256, out_features=64, bias=True)\n",
      "  (tanh): Tanh()\n",
      "  (relu): ReLU()\n",
      ")\n",
      "The model has 953408 trainable parameters\n",
      "\n",
      "Batch/Epoch/Step: 100, Train Loss: 0.4660, Val f1: 0.7772\n",
      "Batch/Epoch/Step: 200, Train Loss: 0.4356, Val f1: 0.7797\n",
      "Batch/Epoch/Step: 300, Train Loss: 0.4240, Val f1: 0.7853\n",
      "Batch/Epoch/Step: 400, Train Loss: 0.4164, Val f1: 0.7766\n",
      "Batch/Epoch/Step: 500, Train Loss: 0.4145, Val f1: 0.7842\n",
      "Batch/Epoch/Step: 600, Train Loss: 0.4065, Val f1: 0.7789\n",
      "Batch/Epoch/Step: 700, Train Loss: 0.4210, Val f1: 0.7858\n",
      "Batch/Epoch/Step: 800, Train Loss: 0.3985, Val f1: 0.7904\n",
      "Batch/Epoch/Step: 900, Train Loss: 0.3947, Val f1: 0.7868\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test performance \n",
      "\n",
      "Precision : 0.6839962417788913\n",
      "Recall : 0.926208651399491\n",
      "F1 score : 0.7868852459016393\n",
      "ROC AUC Score : 0.7491518235793044\n"
     ]
    }
   ],
   "source": [
    "%run GNN.py --graph enhanced --gnn gcn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbbb9728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of node features: 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANDRE\\Anaconda3\\envs\\GNNs\\lib\\site-packages\\torch_geometric\\deprecation.py:22: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
      "  warnings.warn(out)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size : 80204 edges\n",
      "Validation set size : 2358 edges\n",
      "Test set size : 4716 edges\n",
      "\n",
      "GAE(\n",
      "  (linear1): Linear(in_features=15, out_features=64, bias=True)\n",
      "  (linear2): Linear(in_features=64, out_features=256, bias=True)\n",
      "  (linear3): Linear(in_features=256, out_features=1024, bias=True)\n",
      "  (conv): GAT(1024, 256, num_layers=2)\n",
      "  (linear4): Linear(in_features=256, out_features=64, bias=True)\n",
      "  (tanh): Tanh()\n",
      "  (relu): ReLU()\n",
      ")\n",
      "The model has 954944 trainable parameters\n",
      "\n",
      "Batch/Epoch/Step: 100, Train Loss: 0.4876, Val f1: 0.7802\n",
      "Batch/Epoch/Step: 200, Train Loss: 0.4474, Val f1: 0.7742\n",
      "Batch/Epoch/Step: 300, Train Loss: 0.4316, Val f1: 0.7723\n",
      "Batch/Epoch/Step: 400, Train Loss: 0.4233, Val f1: 0.7781\n",
      "Batch/Epoch/Step: 500, Train Loss: 0.4150, Val f1: 0.7821\n",
      "Batch/Epoch/Step: 600, Train Loss: 0.4146, Val f1: 0.7716\n",
      "Batch/Epoch/Step: 700, Train Loss: 0.4034, Val f1: 0.7782\n",
      "Batch/Epoch/Step: 800, Train Loss: 0.4009, Val f1: 0.7735\n",
      "Batch/Epoch/Step: 900, Train Loss: 0.3987, Val f1: 0.7720\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test performance \n",
      "\n",
      "Precision : 0.6939107782481276\n",
      "Recall : 0.9037319762510602\n",
      "F1 score : 0.785043286056364\n",
      "ROC AUC Score : 0.7525445292620865\n"
     ]
    }
   ],
   "source": [
    "%run GNN.py --graph enhanced --gnn gat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55ed5cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of node features: 15\n",
      "Training set size : 80204 edges\n",
      "Validation set size : 2358 edges\n",
      "Test set size : 4716 edges\n",
      "\n",
      "GAE(\n",
      "  (linear1): Linear(in_features=15, out_features=64, bias=True)\n",
      "  (linear2): Linear(in_features=64, out_features=256, bias=True)\n",
      "  (linear3): Linear(in_features=256, out_features=1024, bias=True)\n",
      "  (conv): GraphSAGE(1024, 256, num_layers=2)\n",
      "  (linear4): Linear(in_features=256, out_features=64, bias=True)\n",
      "  (tanh): Tanh()\n",
      "  (relu): ReLU()\n",
      ")\n",
      "The model has 1608768 trainable parameters\n",
      "\n",
      "Batch/Epoch/Step: 100, Train Loss: 0.4647, Val f1: 0.7584\n",
      "Batch/Epoch/Step: 200, Train Loss: 0.4456, Val f1: 0.7607\n",
      "Batch/Epoch/Step: 300, Train Loss: 0.4243, Val f1: 0.7581\n",
      "Batch/Epoch/Step: 400, Train Loss: 0.4181, Val f1: 0.7624\n",
      "Batch/Epoch/Step: 500, Train Loss: 0.4101, Val f1: 0.7608\n",
      "Batch/Epoch/Step: 600, Train Loss: 0.4160, Val f1: 0.7656\n",
      "Batch/Epoch/Step: 700, Train Loss: 0.4022, Val f1: 0.7648\n",
      "Batch/Epoch/Step: 800, Train Loss: 0.4102, Val f1: 0.7703\n",
      "Batch/Epoch/Step: 900, Train Loss: 0.3978, Val f1: 0.7686\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test performance \n",
      "\n",
      "Precision : 0.6737007874015748\n",
      "Recall : 0.9071246819338422\n",
      "F1 score : 0.7731791071751309\n",
      "ROC AUC Score : 0.7338846480067853\n"
     ]
    }
   ],
   "source": [
    "%run GNN.py --graph enhanced --gnn sage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "953d5859",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "GNNs",
   "language": "python",
   "name": "gnns"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
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