tweet_similarity_ranking.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tweet similarity ranking\n",
    "\n",
    "### Problem:\n",
    "\n",
    "1: Use Twitter API to collect 1000 tweets in which keyword ‘narendra modi’ appears, save the collected tweets in nm.txt \n",
    "\n",
    "2: Convert the collected tweets into BoW vectors and find cosine similarity of a pair of tweets and print the top-10 most similar tweet pairs, print these pairs\n",
    "\n",
    "3: Do the same using TF-IDF vectors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect and store the data in nm.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tweepy\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from secrets import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connect twitter API\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
    "auth.set_access_token(access_token, access_token_secret)\n",
    "api = tweepy.API(auth, wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "search_words = \"narendra modi\"\n",
    "no_of_tweets = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ignoring retweets to avoid duplicacy\n",
    "\n",
    "new_search = \"narendra modi -filter:retweets\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets = tweepy.Cursor(api.search,\n",
    "                  q=new_search).items(no_of_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding tweet text to nm.txt\n",
    "def text_to_file(tweets, file_name):\n",
    "    with open(data_path+file_name, \"w\") as f:\n",
    "        for tweet in tweets:\n",
    "            f.write(tweet.text + '\\n\\n ----------------------\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert tweets data into dataframe\n",
    "\n",
    "text_details = [[tweet.id, tweet.created_at, tweet.user.screen_name, tweet.text] for tweet in tweets]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Save tweets to csv\n",
    "\n",
    "with open(data_path + \"nm2.csv\", 'w') as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow(['Tweet_id', 'Created At', 'Created By', 'Tweet Text'])\n",
    "    writer.writerows(text_details)\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process the stored data\n",
    "\n",
    "### Convert the collected tweets into BoW vectors and find cosine similarity of a pair of tweets and print the top-10 most similar tweet pairs, print these pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data from nm.csv\n",
    "\n",
    "df = pd.read_csv(data_path+\"nm2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>creator_id</th>\n",
       "      <th>created_by</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1321587869807341569</td>\n",
       "      <td>@jadafromnola @FriedrichPieter What an amazing...</td>\n",
       "      <td>1279132550380654592</td>\n",
       "      <td>BHARATIYASEEKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1321585634440458240</td>\n",
       "      <td>@JoshCastelino Oh rice bag converts you ppl ha...</td>\n",
       "      <td>396353624</td>\n",
       "      <td>RecepErdoggann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1321580847825264640</td>\n",
       "      <td>PM Narendra Modi dubs Tejashwi ‘jungle raj ke ...</td>\n",
       "      <td>134758540</td>\n",
       "      <td>timesofindia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1321577699861647360</td>\n",
       "      <td>Hindu hi to tha mar gaya \\nMaar diya police ne...</td>\n",
       "      <td>1184757165828927488</td>\n",
       "      <td>randm_indianguy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1321570785706733568</td>\n",
       "      <td>गरीब के नाम पर राजनीति करने वालों ने देश में ऐ...</td>\n",
       "      <td>1274235804525539328</td>\n",
       "      <td>BhagwanSankla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                                               text  \\\n",
       "0  1321587869807341569  @jadafromnola @FriedrichPieter What an amazing...   \n",
       "1  1321585634440458240  @JoshCastelino Oh rice bag converts you ppl ha...   \n",
       "2  1321580847825264640  PM Narendra Modi dubs Tejashwi ‘jungle raj ke ...   \n",
       "3  1321577699861647360  Hindu hi to tha mar gaya \\nMaar diya police ne...   \n",
       "4  1321570785706733568  गरीब के नाम पर राजनीति करने वालों ने देश में ऐ...   \n",
       "\n",
       "            creator_id       created_by  \n",
       "0  1279132550380654592  BHARATIYASEEKER  \n",
       "1            396353624   RecepErdoggann  \n",
       "2            134758540     timesofindia  \n",
       "3  1184757165828927488  randm_indianguy  \n",
       "4  1274235804525539328    BhagwanSankla  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'@jadafromnola @FriedrichPieter What an amazing quote from Narendra Modi. https://t.co/g29xriaEOx'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "texts = df[\"text\"]\n",
    "texts[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add text to python list\n",
    "\n",
    "text_list = []\n",
    "for text in texts:\n",
    "    text_list.append(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(text_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tfidf vectorize\n",
    "\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf_vectorizer = TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = tfidf_vectorizer.fit_transform(text_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4307"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tfidf_vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 4307)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = T.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4307"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(T[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. ... 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "print(T[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_df = pd.DataFrame(T, columns = tfidf_vectorizer.get_feature_names())  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "\n",
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       "2  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[5 rows x 4307 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cosine similarity\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "tweet_similarity = []\n",
    "\n",
    "for i in range(len(T)):\n",
    "    tweet_similarity_row = []\n",
    "    for j in range(len(T)):\n",
    "        a = np.reshape(T[i], (1, T[i].size))\n",
    "        b = np.reshape(T[j], (1, T[j].size))\n",
    "        c = cosine_similarity(a,b)\n",
    "        tweet_similarity_row.append(c[0][0])\n",
    "    tweet_similarity.append(tweet_similarity_row)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweet_similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tweet_similarity[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find pair which are most similar (max value in tweet_similarity)\n",
    "\n",
    "row_col_similarity = []\n",
    "\n",
    "for i in range(len(T)):\n",
    "    for j in range(i, len(T)):\n",
    "        if i != j:\n",
    "            row_col_similarity.append([i, j, tweet_similarity[i][j]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "499500"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(row_col_similarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(row_col_similarity[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the pandas DataFrame \n",
    "similarity_df = pd.DataFrame(row_col_similarity, columns = ['Tweet1 index', 'Tweet2 index', 'cosine similarity']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort in dec order of cosine similarity\n",
    "\n",
    "top_ten_df = similarity_df.nlargest(10, 'cosine similarity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Tweet1 index</th>\n",
       "      <th>Tweet2 index</th>\n",
       "      <th>cosine similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>268</td>\n",
       "      <td>269</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>92</td>\n",
       "      <td>510</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>212</td>\n",
       "      <td>398</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>728</td>\n",
       "      <td>729</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>728</td>\n",
       "      <td>730</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>728</td>\n",
       "      <td>731</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>728</td>\n",
       "      <td>732</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>728</td>\n",
       "      <td>733</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>728</td>\n",
       "      <td>734</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>728</td>\n",
       "      <td>735</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tweet1 index  Tweet2 index  cosine similarity\n",
       "0           268           269                1.0\n",
       "1            92           510                1.0\n",
       "2           212           398                1.0\n",
       "3           728           729                1.0\n",
       "4           728           730                1.0\n",
       "5           728           731                1.0\n",
       "6           728           732                1.0\n",
       "7           728           733                1.0\n",
       "8           728           734                1.0\n",
       "9           728           735                1.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_ten_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_path+\"nm.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>tweet_id</th>\n",
       "      <th>text</th>\n",
       "      <th>creator_id</th>\n",
       "      <th>created_by</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1321587869807341569</td>\n",
       "      <td>@jadafromnola @FriedrichPieter What an amazing...</td>\n",
       "      <td>1279132550380654592</td>\n",
       "      <td>BHARATIYASEEKER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1321585634440458240</td>\n",
       "      <td>@JoshCastelino Oh rice bag converts you ppl ha...</td>\n",
       "      <td>396353624</td>\n",
       "      <td>RecepErdoggann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1321580847825264640</td>\n",
       "      <td>PM Narendra Modi dubs Tejashwi ‘jungle raj ke ...</td>\n",
       "      <td>134758540</td>\n",
       "      <td>timesofindia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1321577699861647360</td>\n",
       "      <td>Hindu hi to tha mar gaya \\nMaar diya police ne...</td>\n",
       "      <td>1184757165828927488</td>\n",
       "      <td>randm_indianguy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1321570785706733568</td>\n",
       "      <td>गरीब के नाम पर राजनीति करने वालों ने देश में ऐ...</td>\n",
       "      <td>1274235804525539328</td>\n",
       "      <td>BhagwanSankla</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              tweet_id                                               text  \\\n",
       "0  1321587869807341569  @jadafromnola @FriedrichPieter What an amazing...   \n",
       "1  1321585634440458240  @JoshCastelino Oh rice bag converts you ppl ha...   \n",
       "2  1321580847825264640  PM Narendra Modi dubs Tejashwi ‘jungle raj ke ...   \n",
       "3  1321577699861647360  Hindu hi to tha mar gaya \\nMaar diya police ne...   \n",
       "4  1321570785706733568  गरीब के नाम पर राजनीति करने वालों ने देश में ऐ...   \n",
       "\n",
       "            creator_id       created_by  \n",
       "0  1279132550380654592  BHARATIYASEEKER  \n",
       "1            396353624   RecepErdoggann  \n",
       "2            134758540     timesofindia  \n",
       "3  1184757165828927488  randm_indianguy  \n",
       "4  1274235804525539328    BhagwanSankla  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similar tweets pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tweet pair  1\n",
      "Tweet1\n",
      "sarahtweets8t\n",
      "Tweet2\n",
      "sarahtweets8t\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  2\n",
      "Tweet1\n",
      "PatelGiraK2\n",
      "Tweet2\n",
      "SunilGu96576773\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  3\n",
      "Tweet1\n",
      "DimitriCruz5\n",
      "Tweet2\n",
      "PTI_News\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  4\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  5\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  6\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  7\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  8\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  9\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n",
      "\n",
      "Tweet pair  10\n",
      "Tweet1\n",
      "blan_sera\n",
      "Tweet2\n",
      "blan_sera\n",
      "************************************************************\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(top_ten_df)):\n",
    "    print(\"\\nTweet pair \", i+1)\n",
    "    t1 = top_ten_df.iloc[i, 0]\n",
    "    t2 = top_ten_df.iloc[i, 1] \n",
    "    print(\"Tweet1\")\n",
    "    print(df.iloc[t1, 3])\n",
    "    print(\"Tweet2\")\n",
    "    print(df.iloc[t2, 3])\n",
    "    print(\"************************************************************\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### The could have been better if duplicate text of tweets were dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Collecting networkx\n",
      "  Downloading networkx-2.4-py3-none-any.whl (1.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.6 MB 33 kB/s  eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: decorator>=4.3.0 in /home/soumya/.local/lib/python3.5/site-packages (from networkx) (4.4.2)\n",
      "Installing collected packages: networkx\n",
      "Successfully installed networkx-2.4\n",
      "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 20.2.4 is available.\n",
      "You should consider upgrading via the '/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install networkx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx \n",
    "# importing matplotlib.pyplot \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = nx.DiGraph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9PXh5eYmOorXc3d2RlJSEwsJCBAcH4+bNm6IjEZGKY/E9gW3btmHMmDFc5hTM3Nwc27dvx/PPP4/AwEAcO3ZMdCQiUmFc6mwihUIBFxcX7Nq1C56enqLj0P/s27cPkydPxnvvvYc33niDP5QQUQOc+JooMTERxsbG8PDwEB2F/mDo0KE4efIkvv/+e0yePBmVlZWiIxGRimHxNVFUVBSXOVVUhw4d8Ntvv0Emk6FPnz64fv266EhEpEJYfE0gl8sRHR3Ne3OqMBMTE2zcuBGTJk1Cr169cODAAdGRiEhFsPia4OTJk2jTpg26desmOgo9hEQiwezZs7F161ZMmjQJixcvBi9pExGLrwmioqI47amR4OBgJCcnIzY2FqNHj0ZZWZnoSEQkEIuvkWQyGWJiYlh8asbJyQlHjhyBpaUlevXqhUuXLomORESCsPga6fjx47C1tYWbm5voKNRIhoaGWLNmDWbPno2+fftix44doiMRkQA816WReOCs+nvllVfg6emJiIgIpKSk4MMPP4SODn8GJNIW3MDeCDKZDI6Ojjh+/Dg6deokOg49ocLCQkRERMDMzAybNm2CpaWl6EhE1Ar4Y24jHDlyBI6Ojiw9DWFnZ4dff/0VnTt3RkBAAM6ePSs6EhG1AhZfI3CZU/Po6elh6dKl+PDDD/H0008jKipKdCQiamFc6nxMdXV1cHBwQGJiIlxdXUXHoRaQlpaG0NBQhIaGYtGiRZBKeQmcSBNx4ntMhw4dgouLC0tPg3l7eyM5ORlnz57FkCFDcPv2bdGRiKgFsPge04MjiEizWVtbY8+ePejZsyf8/f1x6tQp0ZGIqJlxqfMx1NbWwt7eHikpKXB2dhYdh1pJbGwspk+fjiVLlmDKlCmi4xBRM+FFjMfw4J1/LD3tEhoaCnd3d4SEhCA5ORlLly6Fvr6+6FhE9IS41PkYuMypvbp164akpCTk5eUhODgYeXl5oiMR0RNi8T1CTU0N4uPjER4eLjoKCWJhYYHY2FgMHz4cAQEBOH78uOhIRPQEWHyPcODAAXTr1g1OTk6io5BAOjo6eO+99/D9998jLCwMK1as4BFHRGqKxfcID05aJwKAYcOG4bfffsN3332HKVOmoLKyUnQkImokFt9DVFVVYefOnQgLCxMdhVRIx44dcfLkSdTU1KBv377Izs4WHYmIGoHF9xD79++Hl5cXHBwcREchFWNiYoJNmzZhwoQJ6NmzJ3755RfRkYjoMbH4HoInrdPDSCQSzJkzB1u2bMHEiRPx2Wef8bofkRrgBva/UVlZCXt7e1y8eBF2dnai45CKy8nJQVhYGFxcXPDDDz/A1NRUdCQi+huc+P7G3r174efnx9Kjx9KuXTscPXoU5ubm6NWrFy5fviw6EhH9DRbf3+ARRNRYhoaGWLNmDWbMmIE+ffpg165doiMR0V/gUudfqKiogL29Pa5cuQIbGxvRcUgNnTx5EhEREZg6dSo++OAD6OjwZ0wiVcG/jX9h9+7d6NmzJ0uPmqx37944deoUDh48iBdeeAElJSWiIxHR/7D4/gKXOak5PPXUU/j111/h6uqKgIAAnDt3TnQkIgKXOhu4f/8+HB0dkZWVBWtra9FxSENs2LAB//znP7FixQr+UEUkGI8l+pNdu3YhKCiIpUfNauLEifDw8EBoaChOnTqFhQsXQirlXz8iEbjU+Sc8gohaio+PD06dOoW0tDQMHToURUVFoiMRaSUW3x+Ulpbi119/xQsvvCA6Cmkoa2tr7N27FwEBAfD390dKSoroSERah8X3Bzt37kS/fv3Qpk0b0VFIg+nq6uLTTz/FF198gaFDh2LdunWiIxFpFV5k+AMuc1JrCgsLQ9euXRESEoLk5GR8+eWX0NfXFx2LSOPxXZ3/U1JSAmdnZ9y4cQMWFhai45AWuXfvHiZNmoQ7d+4gOjoa9vb2oiMRaTQudf7Pjh07MHDgQJYetToLCwvExcVhyJAhCAgIwG+//SY6EpFGY/H9D48gIpF0dHTw/vvvY/Xq1QgJCcHKlSt5xBFRC+FSJ4Di4mK4uLggNzcXZmZmouOQlrty5QpCQ0Ph5+eHlStXwsjISHQkIo3CiQ9AfHw8Bg0axNIjldCpUyecPHkSlZWV6NevH7Kzs0VHItIoLD5wmZNUj4mJCbZs2YJx48ahZ8+eOHjwoOhIRBpD65c679y5gw4dOiAvLw8mJiai4xA1cPDgQYwfPx7/+te/8K9//QsSiUR0JCK1pvUT34N307H0SFU9/fTTSExMRFRUFMaOHYv79++LjkSk1rS++LjMSeqgffv2OHbsGExMTNC7d29cuXJFdCQitaXVxXf79m0kJydj+PDhoqMQPZKhoSH++9//4rXXXkNQUBB2794tOhKRWtLq4tu+fTuGDRsGY2Nj0VGIHotEIsGrr76K+Ph4TJ8+HfPnz4dcLhcdi0itaHXx8d6cpK6CgoKQnJyM/fv3Y9SoUbh3757oSERqQ2uLr6CgAKdPn8bQoUNFRyFqEnt7exw8eBDOzs4ICAjA+fPnRUciUgtaW3zbt2/HiBEjYGhoKDoKUZPp6+vjm2++wdy5cxEcHIyYmBjRkYhUntYWH5c5SZNMnjwZP//8M9566y288847kMlkoiMRqSyt3MCel5cHDw8P5Ofnw8DAQHQcomZTVFSEcePGAQC2bNmCtm3bCk5EpHq0cuKLiYnByJEjWXqkcdq2bYu9e/fC19cXAQEBSE1NFR2JSOVoZfFt27aNm9ZJY0mlUixevBhLlizBkCFDsH79etGRiFSK1i115uTkwNvbG/n5+dDX1xcdh6hFnT9/HiEhIRgyZAi++OIL/j9PBC2c+GJiYjBq1Ch+AyCt0L17dyQlJeH69et45plnUFBQIDoSkXBaV3xc5iRtY2lpiZ9++gmDBw+Gv78/Tp48KToSkVBatdSZnZ0Nf39/5OXlQU9PT3Qcola3e/duTJkyBfPnz8f06dN5xBFpJa2a+KKjoxESEsLSI601YsQInDhxAsuXL8fUqVNRVVUlOhJRq9Oq4uMRRERA586dkZCQgPv376Nfv364ceOG6EhErUprii8rKwvZ2dkIDg4WHYVIOFNTU2zduhVjxoxBz549cejQIdGRiFqN1hRfdHQ0wsLCIJVKRUchUgkSiQRvvvkmNm7ciMjISHz55ZfQokv+pMW0pviioqJ4b06iv/DMM88gISEBmzdvRmRkJMrLy0VHImpRWlF8ly9fRl5eHvr16yc6CpFKcnZ2xrFjx2BoaIjevXvj6tWroiMRtRitKL5t27YhPDwcurq6oqMQqSwjIyP88MMP+Mc//oGgoCDs3btXdCSiFqE1xcdlTqJHk0gkeO211xAXF4dXXnkFH3/8MeRyuehYRM1K4zewZ2Zm4plnnkFOTg50dLSi54maRX5+PsLDw9G2bVusX78eFhYWoiMRNQuNb4Jt27YhIiKCpUfUSPb29jh06BCcnJwQGBiICxcuiI5E1Cw0vg14b06iptPX18eKFSvw7rvvIjg4GNu3bxcdieiJafRS5/nz5zF06FBkZ2dz4iN6QikpKQgLC8O4ceOwYMECvlmM1JZGt8GDaY+lR/Tk/Pz8kJycjKSkJAwbNgx37twRHYmoSTS2ERQKBZc5iZqZjY0Nfv75Z3h7eyMgIACnT58WHYmo0TS2+M6ePYvKykoEBgaKjkKkUaRSKZYsWYJFixbh2WefxcaNG0VHImoUjb1x5YNpj+eNEbWM0aNHo1u3bggJCUFSUhK++OILHvlFakEjJz4ucxK1Dg8PDyQnJyMrKwvPPPMMCgoKREcieiSNLL60tDTIZDL4+fmJjkKk8SwtLbFjxw48/fTTCAgIQEJCguhIRA+lkcXHZU6i1qWjo4N58+bh22+/xciRI/Hdd9/xiCNSWRq3j0+hUKBjx46IjY2Ft7e36DhEWufy5csYNWoUevfujeXLl8PQ0FB0JCIlGjfxpaSkQCqVwsvLS3QUIq3UuXNnJCYm4t69e+jfvz9ycnJERyJSonHFx2VOIvFMTU3r75MbGBiIw4cPi45EVE+jljoVCgVcXV2xc+dOeHp6io5DRAB++eUXTJgwAW+//TZmz57NH0pJOI2a+JKSkmBkZAQPDw/RUYjofwYNGoSEhARs2LAB48ePR3l5uehIpOU0qvi4zEmkmlxcXHDixAno6ekhKCgIV69eFR2JtJjGFJ9cLuemdSIVZmRkhB9//BHTpk1DUFAQ9u7dKzoSaSmNKb6EhARYWFige/fuoqMQ0d+QSCR4/fXXsX37dkydOhULFiyAXC4XHYu0jMYUX1RUFMaMGSM6BhE9hr59+yI5ORl79uxBaGgoSktLRUciLaIRxSeXyxEdHY2IiAjRUYjoMTk4OODw4cNwcHBAYGAgMjIyREciLaERxXfixAnY2NjA3d1ddBQiagR9fX2sXLkSb7/9Nvr374/Y2FjRkUgLaMSxRFzmJFJvU6ZMgaenJ8LCwnDq1Cl8/PHH0NXVFR2LNJTab2CXyWRwdHTE8ePH0alTJ9FxiOgJ3L59G2PGjIG+vj42b94MKysr0ZFIA6n9UufRo0fh6OjI0iPSADY2Nti/fz88PT3h7++PtLQ00ZFIA6l98XHvHpFmkUql+Oyzz/Dpp59i8ODB2LRpk+hIpGHUeqmzrq4ODg4OSEhIQIcOHUTHIaJmdvbsWYSEhOC5557DZ599Bj09PdGRSAOo9cR3+PBhuLi4sPSINJSnpyeSk5Nx+fJlDBo0CIWFhaIjkQZQ6+LjMieR5mvTpg127tyJ4OBg+Pv7IyEhQXQkUnNqs52h6H41YlJykVlQitKqOpjq62LnlSrs+2eI6GhE1MJ0dHTw0Ucfwc/PDyNHjsSCBQswbdo00bFITan8Nb4zOSVYcfgKjly6DQCorvvDff1ktTAwMECwmw1eG9AJXu0sBaUkotZy8eJFhISEoE+fPli+fDkMDAxERyI1o9LFtzHhOj7Zk4mqOhkellIiAQylupg73B0Term0Wj4iEqOsrAxTpkxBTk4Otm/fDicnJ9GRSI2o7DW+30svA5W1Dy89AFAogMpaGT7Zk4GNCddbJR8RiWNmZobo6GiEhoYiMDAQR44cER2J1IhKTnxnckowdk0CKmtljX6skZ4uoqb1Qg8nLnsSaYMDBw5g4sSJeOeddzBr1iweRE2PpJIT34rDV1BV1/jSA4CqOhlWHr7SzImISFUNHjwYJ0+exLp16zBhwgRUVFSIjkQqrkWLb/HixXB0dISZmRnc3Nzw66+/orq6GrNnz4aDgwMcHBwwe/ZsVFdXAwB+/PFH9OwdhCOXbtcvb2Yveg61xXkAgKJdX+HOzytRuO1D3PgiHAUb3oLsfjHu/rIaOV+Nwc3V/0B1/lUcungb8xZ8irCwMKU8M2fOxKxZswAAa9euRdeuXWFmZoYOHTrgu+++a8kvBRG1IFdXV5w4cQK6urro3bs3srKyREciFdZixXfx4kUsX74cycnJKCsrw88//wwXFxd88sknSEhIQFpaGs6cOYOkpCQsWLCg/nF37tc89HkrMo/Dsv9EtJu1GdDVQ/6GN6Fv1xFOszbD2L0P7h78HhIAxt2DsW/fPpSUlAD4/S4vW7duxaRJkwAAtra22LVrF0pLS7F27VrMmTMHqampLfXlIKIWZmxsjHXr1mHq1Kno3bs39u3bJzoSqagWKz5dXV1UV1fjwoULqK2thYuLCzp27IhNmzbhgw8+gK2tLWxsbPDhhx9iw4YN9Y+rqJUpb1n4E+MuvWDwVCdIpPow7tIbEqkeTD2fgURHFybu/VBTmIWqOjnya4zQv39/REdHAwD27duHtm3bws/PDwAwYsQIdOzYERKJBAMGDMCzzz6LY8eOtdSXg4hagUQiwYwZMxATE4OXX34ZCxcuhAq+jYEEa7Hi69SpE5YuXYp58+bB1tYWY8eORV5eHvLy8uDs7Fz/cc7OzsjLy6v/vUz+8P9JdUza1P+zRE8fusaWSr9X1FQCAEqrajF58mRs3LgRALBx40ZMnDix/mP37t2LXr16wcrKCpaWltizZw+Kioqe7JMmIpXQr18/JCUlYefOnQgLC0NpaanoSKRCWvQaX2RkJI4fP47s7GxIJBK8/fZwJJxqAAATBElEQVTbcHBwQHZ2dv3H3LhxAw4ODgAAExMTKGqr6/9Mdr+4ya994+pFmJqa4syZMzh37hx27dqF8ePHAwCqq6sRFhaGN998E4WFhSgpKcHw4cP5kyGRBnF0dMThw4dhZ2eHnj17IjMzU3QkUhEteo3v4MGDqK6uhqGhIYyMjKCjo4Nx48ZhwYIFuH37NoqKijB//nxMmDABAODl5YXim1ehKLoGRV0NSo437TgSqUQBw8oifP7556ioqICvry+MjY2xYcMG7Nu3D3l5eaiuroaNjQ2kUin27t2L/fv3N+enT0QqwMDAAN9++y3eeust9O/fH/Hx8aIjkQposXt1VldX45133kFGRgb09PQQFBSE1atXw8rKCqWlpejRowcAICIiAu+99x4AoEuXLnjr3blY/NlcSKT6sBwwGffTGn+BWldXFz/+51VYm87G0aNHMWDAAISGhqKkpARLlixBamoqzM3NMWTIEEgkEvTu3RvPPvtss37+RKQ6XnrpJXh6eiIsLAzJycmYP38+dHV1RcciQVRyA/u0DadwIKPwkXds+SsSCTCkmx1WTfAH8PtSqru7OwoKCmBubg4AUCgUyMrKQkpKClJTU5GSkoKUlBQYGRnBz88Pvr6+8PPzg5+fH+zt7bkhlkhD3Lp1C2PGjIGhoSE2bdoEKysr0ZFIAJUsvua6c4tcLsc///lPlJaW4ocffnjo4xQKBa5fv65UhCkpKZBKpfUl+KAQnZycWIZEaqqurg5vv/024uPjERsbCy8vL9GRqJWpZPEBf7xX599vbfgzIz0dzB3eFRN6uaC8vBx2dnZwdnbGvn370K5du0ZnUCgUyMnJaTAZKhSKBpNh+/btWYZEamTLli2YOXMmli1bhsjISNFxqBWpbPEBqnk6g0KhwM2bN5WKMDU1FTU1NUpF6OvrC1dXV5YhkQpLT09HSEgIRo4ciSVLlkBPT090JGoFKl18AJCeW4KVh6/g0MXbkACo+sPmdkOpDhQABrrZ4LXgTkJvTJ2fn99gMnzwjtI/FmKHDh2go6OSt0gl0krFxcUYP348KioqEBUVBTs7O9GRqIWpfPE9cOd+NWJSc5GZX4bSqlqYG+rB3d4M4b5OsDZVzYMoCwsLG0yG9+7dg4+Pj9JSaefOnVmGRALJZDLMmzcP69atQ0xMDAIDA0VHohakNsWnKW7fvo3U1FSlQrxz5w58fHyUJsMuXbrw7dZErSw+Ph7Tpk3DwoULMXXqVNFxqIWw+FTA3bt3G0yGhYWF8PLyUrpm6O7uDqm0xbZeEhGAzMxMhISEoH///vj6669hYKCaK0rUdCw+FVVcXIzTp08rFWJeXh569OihNBl27dqVF+SJmllZWRlefPFF3Lx5EzExMXBychIdiZoRi0+N3Lt3D2lpaUqT4Y0bN+Dh4aE0GXbv3h36+vqi4xKpNYVCgcWLF+Prr7/G1q1b0b9/f9GRqJmw+NRcWVkZ0tLSlCbDa9euoXv37kqToYeHB5dsiJpg//79mDhxIubOnYsZM2Zwi5IGYPFpoPLycpw5c0ZpMrxy5Qrc3d2VJsMePXrA0NBQdFwilXft2jWEhoaie/fuWL16NYyNjUVHoifA4tMSFRUVSE9PV5oML126hC5duihNhj169OBfaqK/UFFRgenTp+PcuXOIjY2Fq6ur6EjURCw+LVZVVYWzZ88qTYYZGRno2LGj0mTo7e0NExMT0XGJhFMoFPjmm2/wySefYMOGDTzVRU2x+EhJdXU1zp07pzQZnj9/Hq6urkqTobe3N8zMzETHJRLi6NGjGDt2LGbMmIF33nmH1/3UDIuPHqm2thbnz59XmgzPnj2Ldu3aKU2GPj4+sLCwEB2XqFXk5uYiPDwcDg4OWLduHX8QVCMsPmqS2tpaZGRkKE2G6enpcHBwUJoMfXx80KZNG9FxiVpEdXU1Zs6ciaNHjyI+Ph5ubm6iI9FjYPFRs6mrq8PFixeVJsO0tDTY2toqTYa+vr6wtrYWHZeo2Xz//ff4z3/+g9WrV2PUqFGi49AjsPioRclkMly6dElpMjx9+jSsra0bHONkY2MjOi5RkyUmJiIiIgKTJk3CRx99xHvtqjAWH7U6uVyOK1euKE2GqampMDc3b3DAL4+IIXVSWFiIMWPGwMjICJs3b+Yyv4pi8ZFKkMvlyMrKanCzbmNj4waToYODg+i4RH+rtrYW//73v7Fjxw7ExcWhR48eoiPRn7D4SGUpFApcv35dqQhTUlKgp6enVIR+fn5wdHTkW8pJpWzevBmzZs3C119/jXHjxomOQ3/A4iO1olAocOPGDaXJMCUlBRKJpMFk2L59e5YhCXXmzBmEhobihRdewJIlS3ismIpg8ZHaUygUuHnzZoPJsK6urr4MH/zq4uLCMqRWdffuXURGRqK6uhpRUVGwtbUVHUnrsfhIY+Xl5TWYDCsrKxtMhh07dmQZUouSyWT48MMPsX79esTExCAwMFB0JK3G4iOtUlBQUF+GD34tLS1tMBl26tQJOjo6ouOShomLi8O0adOwaNEivPzyy6LjaC0WH2m927dvN5gMi4uL4e3tXT8Z+vn5oXPnztybRU8sMzMTISEhGDBgAJYtW8ZzMgVg8RH9hTt37jSYDG/dulVfhg8mQ3d3d5YhNVppaSlefPFF5OfnIyYmBo6OjqIjaRUWH9FjKi4uxunTp5Umw/z8fPTo0UPpmmG3bt347j16JLlcjkWLFmHFihXYunUr+vXrJzqS1mDxET2Be/fu1Zfhg8kwJycHnp6eSpNh9+7doaenJzouqaB9+/Zh8uTJmDt3LmbMmME3WrUCFh9RMysrK0NaWprS9orr16+jW7duSpOhh4cHr+8QACArKwuhoaHo0aMHVq1aBWNjY9GRNBqLj6gVlJeX15fhg8nw6tWr6Nq1q9Jk6OnpCUNDQ9FxSYCKigq88soruHDhAuLi4uDi4iI6ksZi8REJUlFRgfT0dKVrhpcvX0aXLl2UJkMvLy8YGRmJjkutQKFQYNmyZVi0aBE2bNiAwYMHi46kkVh8RCqkqqqqvgwfTIaZmZno1KmT0mTo5eUFExMT0XGphRw5cgTjxo3DzJkz8fbbb/O6XzNj8RGpuOrqapw9e1Zpr+GFCxfg6uqqNBn6+PjA1NRUdFxqJrm5uQgLC0O7du2wdu1amJmZiY6kMVh8RGqopqYG58+fV5oMz507h/bt2ytNhj4+PjA3Nxcdl5qouroab7zxBk6cOIG4uDi4ubmJjqQRWHxEGqK2thYZGRlK1wzT09Ph6OioNBn6+vrC0tJSdFxqhDVr1mDu3Ln4/vvvMXLkSNFx1B6Lj0iD1dXVITMzU2kyPHPmDOzs7JQmQ19fX1hZWYmOSw+RkJCAiIgITJkyBfPmzeO9ZJ8Ai49Iy8hkMly6dElpMkxLS4O1tXWDA37btm0rOi79QWFhIUaPHg1TU1Ns3LgRbdq0ER1JLbH4iAhyuRyXL19WmgxTU1NhaWmpVIR+fn48T06w2tpavPXWW9i1axfi4uLg6ekpOpLaYfER0V+Sy+XIyspSmgxTU1NhYmKidGqFr68v7O3tRcfVOhs3bsScOXPwzTffYOzYsaLjqBUWHxE9NoVCgWvXrilNhikpKTAwMGgwGTo4OHD/WQtLS0tDaGgoQkNDsWjRIt4c/TGx+IjoiSgUCmRnZzc401BHR6fBNcN27dqxDJvZ3bt3MW7cONTW1iIqKgo2NjaiI6k8Fh8RNTuFQoHc3NwGk6FMJmswGTo7O7MMn5BMJsP777+PTZs2ISYmBgEBAaIjqTQWHxG1CoVCgfz8/AbXDKuqqpS2Vfj5+aFDhw4swyaIjY3F9OnTsXjxYrz00kui46gsFh8RCVVQUNBgMrx//379ZvsHk2HHjh25d+0xZGRkICQkBAMHDsSyZcugr68vOpLKYfERkcq5deuWUhGmpqaiuLgYPj4+SpNhly5dWIZ/obS0FJMmTcKtW7cQExMDBwcH0ZFUCouPiNRCUVERUlNTlQqxqKgI3t7eSpOhm5sbdHV1RccVTi6XY+HChfj2228RFRWFvn37io6kMlh8RKS2iouLG0yG+fn58PLyUnpHadeuXbX2rf579+7Fiy++iPfffx+vv/46r52CxUdEGqakpASnT59WKsSbN2/C09NTaTLs1q0b9PT0RMdtFVevXkVISAh8fHywatUqrT/YmMVHRBqvtLQUaWlpSpNhdnY2unfvrnTN0MPDQ2PfDFJeXo5XXnkFmZmZiI2NhYuLi+hIwrD4iEgr3b9/H2lpaUqTYVZWFrp166Y0GXp6esLAwEB03GahUCiwdOlSLF68GBs3bsSgQYNERxKCxUdE9D/l5eVIT09XmgwvX74MNzc3pWuGPXr0UOvlwkOHDiEyMhJz5szBW2+9pXXX/Vh8REQPUVlZifT0dKXJ8OLFi+jcubPSZOjl5QVjY2PRcR9bTk4OwsLC4OzsjB9++AFmZmaiI7UaFh8RUSNVV1fj7NmzSpPhhQsX0KFDB6Vrht7e3jA1NRUd929VVVXhjTfewMmTJxEXF4cuXbqIjtQqWHxERM2gpqYG586dU5oMz58/D2dnZ6XJ0NvbG+bm5qLj1lMoFFi9ejXef/99/Pe//8Xzzz8vOlKLY/EREbWQ2tpaXLhwQWkyTE9Ph5OTk9I1Q19fX1hYWAjNmpCQgIiICLz00kv48MMPERcXh5SUFCxcuFBorpbA4iMiakV1dXXIyMhQmgzPnDkDe3t7pcnQx8cHVlZWrZqtoKAAo0ePhkKhQEpKCuRyOa5cuQInJ6dWzdHSWHxERILJZDJcvHhRaTJMS0tD27ZtGxzjZG1t3aJZbt26BVdXV1RUVEBPTw/Tpk3D8uXLG3xc0f1qxKTkIrOgFKVVdTA3lML9KXNE+DnB2lS1t3+w+IiIVJBcLselS5eUJsPTp0+jTZs2SkXo6+sLW1vbZnvd8PBwxMXFQS6XAwD09fWRn59fP32eySnBisNXcOTSbQBAdZ28/rGGUh0oAAS72eC1AZ3g1c6y2XI1JxYfEZGakMvluHr1qtJkmJqaClNT0wan3T/11FNNeo3ExETs2LEDhw4dwunTp1FVVYXJkyfjxx9/xMaE6/hkTyaq6mR4WHNIJIChVBdzh7tjQi+Xpn2yLYjFR0SkxhQKBbKyshrcrNvQ0LDBZOjg4PDQzeppaWnw8PCov6G3QqHA2bNnYWFhgWP5CnyyJwOVtfK/ffyfGenpYO7wripXfiw+IiINo1AokJ2drVSEKSkp0NXVbXDN0MnJCRKJBDU1NTA1NYVEIsHGjRsRERFR/3xnckowdk0CKmtljc5ipKeLqGm90MNJdZY9WXxERFpAoVAgJydHaTJMSUmBQqGAr68vHB0dsXnzZlRVVcHMzAz79u1DUFAQAGDahlM4kFH40OXNvyORAEO62WHVBP9m/oyajsVHRKSlFAoF8vLykJKSgjVr1mD37t34YyUMHDgQphZtsOfAIcjrqqFv4wqrIa9B38YZAFC06ytI9A0hu1eIqpzz0LNuh7Yj34JeG3vUlRTi5qqX0f7fP8FQXw+/vf00wp4bggkTJmDq1KmiPmUAgI7QVyciImEkEgkcHR0xcuRItG3bFjo6OpBIJDAzM8PAgQPxxhtvwLJLAFxfW4N2MzZB/6mOKNr5udJzVFw4Cos+49Bu9lZI29ij5Oj6hq8DICY1t5U+q0dj8REREUaMGIGlS5fCwcEB27dvx8GDBxEaGgor3yGo1TWERKoHy76RqL11DfKq8vrHGXfpDQMHN0h0dGHaPRg1hdcaPHdVnRyZ+WWt+ek8lFR0ACIiEi88PBwA8Pnnn9e/81Mmk+Hg+qW4+dt+yCruAZLfZyVZZSl0DE0AADqmbeqfQyI1gKK28i+fv7SqtiXjNwqLj4iI6v1xu8PmzZuRm3YUtmMXQGphB0V1OXKWjgXw6LeGSPQNAQCK2mpIDIxhbqiHgoKClordKFzqJCKienZ2dsjKygIAlJWVwcTICEZmllDUVqP4SMPrd39H19gCumbWKD9/CAY6CtxJ3YerV6+2VOxGYfEREVG9d999FwsWLIClpSXu3r0LD7eOyFo2EXnfvwoDB7dGPZf10Bm4lxiLK1+MhWlFQf32CNG4nYGIiB5K0/bxceIjIqKHej24Ewyluk16rKFUF68Fd2rmRE+GxUdERA/l1c4Sc4e7w0ivcZXx+7063VXqdmUA39VJRESP4cGNpnk6AxERaZX03BKsPHwFhy7ehgS/b05/4MF5fAPdbPBacCeVm/QeYPEREVGj3blfjZjUXGTml6G0qhbmhnpwtzdDuC9PYCciIlIpfHMLERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFpFRYfERFplf8DMpbzKzLPBkAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g.add_edge(\"soumya\", \"tanu\")\n",
    "g.add_edge(\"soumya\", \"vaibhav\")\n",
    "g.add_edge(\"vaibhav\", \"tanu\")\n",
    "g.add_edge(\"vaibhav\", \"tanu\")\n",
    "nx.draw(g, with_labels = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = nx.DiGraph()\n",
    "s = [1, 8]\n",
    "ff = []\n",
    "a = [1, 2, 3]\n",
    "b = [5, 6, 7]\n",
    "ff = [a, b]\n",
    "ff\n",
    "gg = []\n",
    "gg.append(ff)\n",
    "\n",
    "d = [8, 4, 3]\n",
    "e = [4, 8, 5]\n",
    "k = [d, e]\n",
    "gg.append(k)\n",
    "user_list = gg\n",
    "unique_creators = s\n",
    "for i in range(len(user_list)):\n",
    "    for j in range(len(user_list[i][0])):\n",
    "        g.add_edge(user_list[i][0][j], unique_creators[i])\n",
    "    \n",
    "    for k in range(len(user_list[i][1])):\n",
    "        g.add_edge(unique_creators[i], user_list[i][1][k])\n",
    "        \n",
    "nx.draw(g, with_labels = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find popular users in this G based on\n",
    "\n",
    "Degree centrality\n",
    "\n",
    "Betweeness centrality\n",
    "\n",
    "Closeness centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 1.0,\n",
       " 2: 0.14285714285714285,\n",
       " 3: 0.2857142857142857,\n",
       " 4: 0.2857142857142857,\n",
       " 5: 0.2857142857142857,\n",
       " 6: 0.14285714285714285,\n",
       " 7: 0.14285714285714285,\n",
       " 8: 0.8571428571428571}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree_centrality(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}