day_21_alice_bipartite_projection.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7423be59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# for the explode statement; convert string to list\n",
    "from ast import literal_eval\n",
    "\n",
    "# for bipartite projection\n",
    "from networkx.algorithms import bipartite\n",
    "\n",
    "# for text and NER\n",
    "from tqdm import tqdm\n",
    "import spacy\n",
    "import requests\n",
    "\n",
    "nlp = spacy.load(\"en_core_web_md\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38a1e588",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(): \n",
    "\n",
    "    url = 'https://www.gutenberg.org/files/11/11-0.txt' \n",
    "    text = requests.get(url).text \n",
    "\n",
    "    # strip header junk \n",
    "    cutoff = text.index('Alice was beginning') \n",
    "    text = text[cutoff:] \n",
    "\n",
    "    # strip footer junk \n",
    "    cutoff = text.rindex('THE END') \n",
    "    text = text[:cutoff] \n",
    "\n",
    "    # pre-processing to clean the text \n",
    "    text = text.replace('\\r', ' ').replace('\\n', ' ') \n",
    "    text = text.replace('â\\x80\\x99', '\\'').replace('â\\x80\\x9c', '\"').replace('â\\x80\\x9d', '\"\"').replace('â\\x80\\x94', ' ') \n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c88298e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_entities(text): \n",
    "\n",
    "    doc = nlp(text) \n",
    "\n",
    "    sentences = list(doc.sents) \n",
    "\n",
    "    entities = [] \n",
    "\n",
    "    for sentence in tqdm(sentences): \n",
    "\n",
    "        sentence_entities = [] \n",
    "\n",
    "        sent_doc = nlp(sentence.text) \n",
    "\n",
    "        for ent in sent_doc.ents: \n",
    "\n",
    "            if ent.label_ in ['PERSON', 'ORG', 'GPE']: \n",
    "\n",
    "                entity = ent.text.strip() \n",
    "\n",
    "                if \"'s\" in entity: \n",
    "\n",
    "                    cutoff = entity.index(\"'s\") \n",
    "\n",
    "                    entity = entity[:cutoff] \n",
    "\n",
    "                if entity != '': \n",
    "\n",
    "                    sentence_entities.append(entity)   \n",
    "\n",
    "        sentence_entities = list(set(sentence_entities))       \n",
    "\n",
    "        if len(sentence_entities) > 0: # changed from 1 to zero to grab individuals from sentences; new idea after book\n",
    "\n",
    "            entities.append(sentence_entities) \n",
    "\n",
    "    return entities "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "102626ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for visualization\n",
    "from IPython.display import SVG \n",
    "from sknetwork.visualization import svg_graph \n",
    "from sknetwork.data import Bunch \n",
    "from sknetwork.ranking import PageRank \n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "def draw_graph(G, show_names=False, node_size=1, font_size=10, edge_width=0.5):\n",
    "\n",
    "    adjacency = nx.to_scipy_sparse_array(G, nodelist=None, dtype=None, weight='weight', format='csr')\n",
    "    adjacency = csr_matrix(adjacency) # fix to weird sknetwork-csr issue; comment out to troubleshoot\n",
    "    \n",
    "    names = np.array(list(G.nodes))\n",
    "    \n",
    "    graph = Bunch()\n",
    "    graph.adjacency = adjacency\n",
    "    graph.names = np.array(names)\n",
    "    \n",
    "    pagerank = PageRank()\n",
    "    scores = pagerank.fit_transform(adjacency)\n",
    "\n",
    "    if show_names:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, font_size=font_size, node_size=node_size, names=graph.names, width=700, height=500, scores=scores, edge_width=edge_width)\n",
    "    \n",
    "    else:\n",
    "        \n",
    "        image = svg_graph(graph.adjacency, node_size=node_size, width=700, height=500, scores = scores, edge_width=edge_width)\n",
    "    \n",
    "    return SVG(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a12aec",
   "metadata": {},
   "source": [
    "# Get Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "443c3cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alice was beginning to get very tired of sitting by her sister on the  bank, and of having nothing to do: once or twice she had peeped into  the book her sister was reading, but it had no pictures or  conversations in it, \"and what is the use of a book,\"\" thought Alice  \"without pictures or conversations?\"\"    So she was considering in her own mind (as well as she could, for the  hot day made her feel very sleepy and stupid), whether the pleasure of  making a daisy-chain would be worth the trouble of getting up and  picking the daisies, when suddenly a White Rabbit with pink eyes ran  close by her.    There was nothing so _very_ remarkable in that; nor did Alice think it  so _very_ much out of the way to hear the Rabbit say to itself, \"Oh  dear! Oh dear! I shall be late!\"\" (when she thought it over afterwards,  it occurred to her that she ought to have wondered at this, but at the  time it all seemed quite natural); but when the Rabbit actually _took a  watch out of its waistcoat-pocket_, and looked at it, and then hurried  on, Alice started to her feet, for it flashed across her mind that she  had never before seen a rabbit with either a waistcoat-pocket, or a  watch to take out of it, and burning with curiosity, she ran across the  field after it, and fortunately was just in time to see it pop down a  large rabbit-hole under the hedge.    In another moment down went Alice after it, never once considering how  in the world she was to get out again.    The rabbit-hole went straight on like a tunnel for some way, and then  dipped suddenly down, so suddenly that Alice had not a moment to think  about stopping herself before she found herself falling down a very  deep well.    Either the well was very deep, or she fell very slowly, for she had  plenty of time as she went down to look about her and to wonder what  was going to happen next. First, she tried to look down and make out  what she was coming to, but it was too dark to see anything; then she  looked at the sid'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text = get_data()\n",
    "text[0:2000]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7984710a",
   "metadata": {},
   "source": [
    "# Extract Entities by Sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a599e622",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_entities(entities):\n",
    "\n",
    "    keep_entities = []\n",
    "\n",
    "    for entity_list in entities:\n",
    "\n",
    "        if entity_list not in keep_entities:\n",
    "            keep_entities.append(entity_list)\n",
    "\n",
    "    return keep_entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dd5ac387",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1126/1126 [00:28<00:00, 39.47it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[['Alice'],\n",
       " ['Alice', 'Longitude'],\n",
       " ['Antipathies'],\n",
       " ['Australia', 'New Zealand'],\n",
       " ['Dinah', \"Dinah'll\"]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entities = extract_entities(text)\n",
    "entities = drop_duplicate_entities(entities)\n",
    "entities[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fe330d82",
   "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>entity</th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Longitude</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Antipathies</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Zealand</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dinah</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dinah'll</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dinah</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alice</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        entity  sentence\n",
       "0        Alice         0\n",
       "1        Alice         1\n",
       "1    Longitude         1\n",
       "2  Antipathies         2\n",
       "3    Australia         3\n",
       "3  New Zealand         3\n",
       "4        Dinah         4\n",
       "4     Dinah'll         4\n",
       "5        Dinah         5\n",
       "6        Alice         6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entity_df = pd.DataFrame({'entities':entities}).reset_index()\n",
    "entity_df.columns = ['sentence', 'entity']\n",
    "entity_df = entity_df.explode('entity')\n",
    "\n",
    "entity_df = entity_df[['entity', 'sentence']]\n",
    "\n",
    "# wrangling\n",
    "entity_df = entity_df[entity_df['entity'].str.contains('CHAPTER', case=False)==False] # drop chapter nodes\n",
    "\n",
    "entity_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "854361a8",
   "metadata": {},
   "source": [
    "# Graph Approach: Bipartite Projection (New Idea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c60d712e",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = nx.from_pandas_edgelist(entity_df, source='entity', target='sentence') # new approach\n",
    "G = bipartite.projected_graph(B, entity_df['entity'])\n",
    "\n",
    "# wrangling\n",
    "G = nx.k_core(G, 1) # quickly remove disconnected junk nodes\n",
    "\n",
    "drop_nodes = ['Beautiful', 'Hm', 'Improve', 'Longitude', 'Ma\\'am', 'Mystery', 'Said', 'Seaography', 'Somebody', \n",
    "              'The Rabbit Sends', 'garden_.', 'The Rabbit Sends', 'the White Rabbit', \"â\\x80\\x98I'll\", 'â\\x80\\x98Keep', \n",
    "              'â\\x80\\x98it', 'William_']\n",
    "\n",
    "G.remove_nodes_from(drop_nodes) # remove drop nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "145f5a30",
   "metadata": {},
   "outputs": [
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x=\"482\" y=\"281\" font-size=\"10\">Lizard</text><text text-anchor=\"start\" x=\"495\" y=\"358\" font-size=\"10\">Turtle</text><text text-anchor=\"start\" x=\"541\" y=\"353\" font-size=\"10\">Wonderland</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_graph(G, show_names=True, node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "95b726e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ada',\n",
       " 'Adventures',\n",
       " 'Alice',\n",
       " 'Arithmetic Ambition',\n",
       " 'Australia',\n",
       " 'Bill',\n",
       " 'Brandy',\n",
       " 'CHORUS',\n",
       " 'Canterbury',\n",
       " 'Caterpillar',\n",
       " 'Cheshire',\n",
       " 'Derision',\n",
       " 'Dinah',\n",
       " \"Dinah'll\",\n",
       " 'Dormouse',\n",
       " 'Duchess',\n",
       " 'Edgar',\n",
       " 'Edwin',\n",
       " 'Elsie',\n",
       " 'England']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use this to look through nodes, twenty at a time; change indexes to look around; learn pandas indexing\n",
    "# useful for identifying junk nodes to remove\n",
    "\n",
    "sorted(G.nodes)[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe51bc4",
   "metadata": {},
   "source": [
    "# New Approach, But Inspecting Sentences\n",
    "\n",
    "This isn't meant to be immediately useful. I am just curious to see if I can see what parts of the network are active based on the sentence number of the sentence that entities were extracted from. If so, that seems like it could be useful, somehow, someday, with more thought.\n",
    "\n",
    "It's neat that there is a core to this and that the network has been projected differently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f4b6795",
   "metadata": {},
   "outputs": [],
   "source": [
    "B = nx.from_pandas_edgelist(entity_df, source='entity', target='sentence') # new approach\n",
    "G_sent = bipartite.projected_graph(B, entity_df['sentence'])\n",
    "\n",
    "# wrangling\n",
    "G_sent = nx.k_core(G_sent, 1) # quickly remove disconnected junk nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0501a59b",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draw_graph(G_sent, show_names=True, node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4cc4b66e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "entity_df['sentence'].value_counts()[0:20].plot.barh().invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "818bc339",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_entities_by_sentence(entity_df, sentence):\n",
    "\n",
    "    return sorted(entity_df[entity_df['sentence']==sentence]['entity'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da8caccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Magpie']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_entities_by_sentence(entity_df, 29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "849d67c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alice', 'Stretching']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_entities_by_sentence(entity_df, 84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "92e6b510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_entities_by_sentence(entity_df, 118)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2fc12fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fetch_entities_by_sentence(entity_df, 130)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "695acca3",
   "metadata": {},
   "source": [
    "At the very least, I can see what sentences have the most activity, and sentences can be investigated, this way. Another fusion of NLP and Networks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7badcf7f",
   "metadata": {},
   "source": [
    "# Egocentric Network Analysis\n",
    "\n",
    "As the above visualization shows, this is a complex network. For knowledge discovery, we should use Egocentric Network Analysis to \"zoom in\" on nodes of interest. Any node can be looked at, but I am going to programmatically do this, in this notebook, showing only the top twenty nodes, according to page rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db54136e",
   "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>page_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alice</th>\n",
       "      <td>0.175101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queen</th>\n",
       "      <td>0.043783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hare</th>\n",
       "      <td>0.031434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hatter</th>\n",
       "      <td>0.029541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mouse</th>\n",
       "      <td>0.027376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dormouse</th>\n",
       "      <td>0.027339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duchess</th>\n",
       "      <td>0.019715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morcar</th>\n",
       "      <td>0.017510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Northumbria</th>\n",
       "      <td>0.017510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mercia</th>\n",
       "      <td>0.017510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             page_rank\n",
       "Alice         0.175101\n",
       "Queen         0.043783\n",
       "Hare          0.031434\n",
       "Hatter        0.029541\n",
       "Mouse         0.027376\n",
       "Dormouse      0.027339\n",
       "Duchess       0.019715\n",
       "Morcar        0.017510\n",
       "Northumbria   0.017510\n",
       "Mercia        0.017510"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page_rank = nx.pagerank(G)\n",
    "\n",
    "pr_df = pd.DataFrame().from_dict(page_rank, orient='index')\n",
    "pr_df.columns = ['page_rank']\n",
    "pr_df.sort_values('page_rank', ascending=False, inplace=True)\n",
    "pr_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97a63ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27FmtWbPGrt6kpCS7fhf+u/j3oo0Xar54dsOV3ltfX19bfS1atFCLFi3UpEkTDRs2TElJSXJyctJ3332nb7/9VgkJCXrwwQdtx15Yf+Jq9O7dW/7+/po0aZLy8vI0efLkqx4TAMoCZjYAAHCRN9980+7zjh07dPjwYdvihS1btpS7u7veeOMNu35Hjx7Vpk2b1KFDhyKd51KzDywWi1xcXOTs7GxrO3funF5//fUrvBL7c10YpyhiYmJkGIYGDx6srKysfPuzs7P1wQcfXHEdFosl37+27927V1988cUVjeHq6mr35f748eP53kbRuXNnScoXbPzbpX4PXbt2VUpKiipWrKjw8PB8278fDbgcT09Pde7cWZMmTVJWVpb2799/2WPWrFljN1vjr7/+0gcffKDbbrvN7u8jICBA9957r1566SW9/PLL6tatm90jLVfjwn2++PdmGEa+NzG0aNFCVqtVq1atsmvfuXNnvkdprvbehoSEaPz48dq3b5/tfAXVKUmLFy8u+sUWYvLkyVqwYIGmTp1qW0gSAFA4ZjYAAHCR3bt366GHHtK9996rI0eOaNKkSapWrZqGDRsmSfLz89OUKVM0ceJE9e/fX3369NGJEycUGxsrNzc3TZs2rUjnadSokdasWaNFixapefPmcnJyUnh4uKKiovTss8/q/vvv18MPP6wTJ05o7ty5RZ4SX5CGDRtKkl555RV5e3vLzc1NtWrVKvDRB+mfQGXRokUaNmyYmjdvrkceeUQNGjRQdna2vvnmG73yyitq2LChunXrdkV1dO3aVTNmzNC0adPUtm1b/fDDD3ryySdVq1Yt5eTkFHmMNWvWaNiwYbrnnnt05MgRzZgxQwEBATp48KCt32233aZ+/frpqaee0m+//aauXbvKarXqm2++kYeHhx577DFJ//weVq5cqVWrVql27dpyc3NTo0aNNHLkSL3zzjtq06aNRo0apcaNGysvL09paWn65JNPNGbMGLVo0aLQWgcPHix3d3e1atVKAQEBOn78uOLi4uTr61ukt0Q4OzurU6dOGj16tPLy8jR79mxlZGQoNjY2X98RI0bY6omPjy/SvSyKTp06ydXVVX369NH48eN1/vx5LVq0SKdOnbLrV6FCBY0ePVpxcXEqX7687rrrLh09elSxsbEKCAiQk9P//fuWGfd27NixevnllxUbG6tevXqpbt26uvHGG/XEE0/IMAxVqFBBH3zwQZEfWSmKESNGyMvLSw8//LDOnDmj55577opnLgFAmeLQ5SkBAPgPLvU2Ck9Pz3x927ZtazRo0CBfe82aNY2oqKh8Y37yySdGv379DD8/P9tbJw4ePJjv+FdffdVo3Lix4erqavj6+hrdu3c39u/fb9fnUjUZxj+r999zzz2Gn5+fYbFYjIv/X/KyZcuMOnXqGFar1ahdu7YRFxdnLF261JBk/Pzzz5e8houv+d9vWFiwYIFRq1Ytw9nZ2ZBkxMfHF1jXxZKSkowHH3zQqFGjhuHq6mp4enoazZo1M6ZOnWr8/vvvV1xHZmamMXbsWKNatWqGm5ubERYWZqxdu9Z48MEH7d7MceFtDM8880yBdc2aNcsICgoyrFarUa9ePWPJkiW2NwxcLDc315g/f77RsGFD2++pZcuWxgcffGDrk5qaatxxxx2Gt7e3IcmujjNnzhiTJ0826tSpYzu+UaNGxqhRo4zjx4/b+un/v7Hg35YvX260b9/eqFq1quHq6mrccMMNRq9evYy9e/de8p5ffP2zZ882YmNjjerVqxuurq5Gs2bNjA0bNlzyuKCgIKNevXqFjn2xC2+jeOuttwrt98EHHxhNmjQx3NzcjGrVqhnjxo0zPv7443xvOMnLyzOeeuopW72NGzc21q1bZzRp0iTfW16Kem8v9bdlGIbx4osv2r1148CBA0anTp0Mb29vo3z58sa9995rpKWlGZKMadOm2Y678Lfyxx9/2I134X8DLv5vrKDf7YoVKwwXFxdjwIABRm5ubqH3DgDKMothGEaxJxwAAJQwCQkJGjBggHbt2nXJ59eBkmrv3r1q0qSJXnzxRdssnJLg559/Vt26dTVt2jRNnDjR0eUAAIoRj1EAAACUUikpKTp8+LAmTpyogICAfK9kLU7ffvutVqxYoVtvvVU+Pj764YcfNGfOHPn4+GjQoEEOqwsA4BiEDQAAAKXUjBkz9Prrr6tevXp666235OHh4bBaPD09tXv3bi1dulSnT5+Wr6+v2rVrp6effrrAV2ICAK5vPEYBAAAAAABMxasvAQAAAACAqQgbAAAAAACAqQgbAAAAAACAqVggspTKy8vTr7/+Km9vb1ksFkeXAwAAAAC4zhmGob/++ks33HCDnJwKn7tA2FBK/frrrwoMDHR0GQAAAACAMubIkSOqXr16oX0IG0opb29vSf/8kn18fBxcDQAAAADgepeRkaHAwEDb99HCEDaUUhcenfDx8SFsAAAAAAAUm6I8ys8CkQAAAAAAwFSEDQAAAAAAwFSEDQAAAAAAwFSEDQAAAAAAwFSEDQAAAAAAwFS8jaKUazhtg5ysHo4uAwAAAADwH6TOinJ0CdcEMxsAAAAAAICpCBv+g8TERFksFp0+fVqSlJCQID8/P4fWBAAAAABASUHYUIgdO3bI2dlZkZGRhfbr3bu3fvzxx2KqCgAAAACAko2woRDLli3TY489pm3btiktLe2S/dzd3VWlSpVirAwAAAAAgJKLsOESzp49q9WrV+uRRx5R165dlZCQcMm+BT1G8f777ys8PFxubm6qVKmSevbsaduXlZWl8ePHq1q1avL09FSLFi2UmJh4bS4EAAAAAIBiRthwCatWrVKdOnVUp04dPfDAA4qPj5dhGEU69sMPP1TPnj0VFRWlb775Rhs3blR4eLht/4ABA7R9+3atXLlSe/fu1b333qvIyEgdPHjwWl0OAAAAAADFhldfXsLSpUv1wAMPSJIiIyN15swZbdy4UR07drzssU8//bTuu+8+xcbG2tqaNGkiSUpJSdGKFSt09OhR3XDDDZKksWPHav369YqPj9fMmTMLHDMzM1OZmZm2zxkZGf/52gAAAAAAuJaY2VCAH374QV999ZXuu+8+SZKLi4t69+6tZcuWFen4pKQkdejQocB9X3/9tQzDUGhoqLy8vGzbli1blJKScskx4+Li5Ovra9sCAwOv/MIAAAAAACgGzGwowNKlS5WTk6Nq1arZ2gzDULly5XTq1KnLHu/u7n7JfXl5eXJ2dtaePXvk7Oxst8/Ly+uSx8XExGj06NG2zxkZGQQOAAAAAIASibDhX3JycvTaa69p3rx5uuOOO+z23X333XrzzTfVsGHDQsdo3LixNm7cqAEDBuTb16xZM+Xm5ur333/XbbfdVuS6rFarrFZrkfsDAAAAAOAohA3/sm7dOp06dUqDBg2Sr6+v3b577rlHS5cu1fz58wsdY9q0aerQoYNuvPFG3XfffcrJydHHH3+s8ePHKzQ0VH379lX//v01b948NWvWTH/++ac2bdqkRo0aqUuXLtfy8gAAAAAAuOZYs+Ffli5dqo4dO+YLGqR/ZjYkJSXp66+/LnSMdu3a6a233tL777+vpk2b6vbbb9eXX35p2x8fH6/+/ftrzJgxqlOnju688059+eWXPBYBAAAAALguWIyivs8RJUpGRsY/C0WOXC0nq4ejywEAAAAA/Aeps6IcXUKRXfgemp6eLh8fn0L7MrMBAAAAAACYijUbSrnvYiMumygBAAAAAFCcmNkAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABMRdgAAAAAAABM5eLoAnB1Gk7bICerh6PLAMqM1FlRji4BAAAAKPGY2QAAAAAAAExF2AAAAAAAAExF2AAAAAAAAExVpsKGI0eOaNCgQbrhhhvk6uqqmjVrasSIETpx4oSjSwMAAAAA4LpRZsKGn376SeHh4frxxx+1YsUKHTp0SC+//LI2btyoli1b6uTJk44uEQAAAACA60KZCRseffRRubq66pNPPlHbtm1Vo0YNde7cWZ999pl++eUXTZo0SZJksVi0du1au2P9/PyUkJBg+/zLL7+od+/eKl++vCpWrKju3bsrNTXV7pj4+HjVq1dPbm5uqlu3rl566SXbvtTUVFksFq1Zs0bt27eXh4eHmjRpoi+++OKS9WdmZiojI8NuAwAAAACgJCoTYcPJkye1YcMGDRs2TO7u7nb7/P391bdvX61atUqGYVx2rL///lvt27eXl5eXPv/8c23btk1eXl6KjIxUVlaWJGnJkiWaNGmSnn76aSUnJ2vmzJmaMmWKli9fbjfWpEmTNHbsWCUlJSk0NFR9+vRRTk5OgeeNi4uTr6+vbQsMDPyPdwMAAAAAgGurTIQNBw8elGEYqlevXoH769Wrp1OnTumPP/647FgrV66Uk5OTXn31VTVq1Ej16tVTfHy80tLSlJiYKEmaMWOG5s2bp549e6pWrVrq2bOnRo0apcWLF9uNNXbsWEVFRSk0NFSxsbE6fPiwDh06VOB5Y2JilJ6ebtuOHDlyZTcBAAAAAIBi4uLoAkqCCzMaXF1dL9t3z549OnTokLy9ve3az58/r5SUFP3xxx+2hSgHDx5s25+TkyNfX1+7Yxo3bmz7OSAgQJL0+++/q27duvnOa7VaZbVai35RAAAAAAA4SJkIG4KDg2WxWHTgwAH16NEj3/7vv/9elStXlp+fnywWS77HKbKzs20/5+XlqXnz5nrzzTfzjVO5cmWdP39e0j+PUrRo0cJuv7Ozs93ncuXK2X62WCy28QEAAAAAKM3KRNhQsWJFderUSS+99JJGjRplt27D8ePH9eabb+rRRx+V9E9gcOzYMdv+gwcP6u+//7Z9DgsL06pVq1SlShX5+PjkO5evr6+qVaumn376SX379r2GVwUAAAAAQMlUJtZskKQXXnhBmZmZioiI0Oeff64jR45o/fr16tSpk0JDQzV16lRJ0u23364XXnhBX3/9tXbv3q2hQ4fazUDo27evKlWqpO7du2vr1q36+eeftWXLFo0YMUJHjx6VJE2fPl1xcXFauHChfvzxR+3bt0/x8fF69tlnHXLtAAAAAAAUpzITNoSEhGjXrl2qXbu2evXqpZo1a6pz584KDQ3V9u3b5eXlJUmaN2+eAgMD1aZNG91///0aO3asPDw8bON4eHjo888/V40aNdSzZ0/Vq1dPAwcO1Llz52wzHR566CG9+uqrSkhIUKNGjdS2bVslJCSoVq1aDrl2AAAAAACKk8Uoyvser1PTpk3Ts88+q08++UQtW7Z0dDlXJCMj459XYI5cLSerx+UPAGCK1FlRji4BAAAAcIgL30PT09MLXFbgYmU6bJCk+Ph4paen6/HHH5eTU+mZ6HElv2QAAAAAAK7WlXwPLRMLRBZmwIABji4BAAAAAIDrSun5p3wAAAAAAFAqEDYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTETYAAAAAAABTuTi6AFydhtM2yMnq4egyUMakzopydAkAAAAASjBmNgAAAAAAAFMRNgAAAAAAAFMRNlxCdHS0evToka89MTFRFotFp0+fLvaaAAAAAAAoDQgbillWVpajSwAAAAAA4JoibLgKJ06cUJ8+fVS9enV5eHioUaNGWrFihV2fdu3aafjw4Ro9erQqVaqkTp06SZIOHDigLl26yMvLS1WrVlW/fv30559/OuIyAAAAAAAwFWHDVTh//ryaN2+udevW6bvvvtPDDz+sfv366csvv7Trt3z5crm4uGj79u1avHixjh07prZt26pp06bavXu31q9fr99++029evW65LkyMzOVkZFhtwEAAAAAUBJZDMMwHF1ESRQdHa033nhDbm5udu25ubk6f/68Tp06JT8/v3zHRUVFqV69epo7d66kf2Y2pKen65tvvrH1mTp1qr788ktt2LDB1nb06FEFBgbqhx9+UGhoaL5xp0+frtjY2HztgSNX8+pLFDtefQkAAACUPRkZGfL19VV6erp8fHwK7etSTDWVSu3bt9eiRYvs2r788ks98MADkv4JHmbNmqVVq1bpl19+UWZmpjIzM+Xp6Wl3THh4uN3nPXv2aPPmzfLy8sp3zpSUlALDhpiYGI0ePdr2OSMjQ4GBgf/52gAAAAAAuFYIGwrh6emp4OBgu7ajR4/afp43b57mz5+vBQsWqFGjRvL09NTIkSPzLQL57/AhLy9P3bp10+zZs/OdMyAgoMBarFarrFbrf70UAAAAAACKDWHDVdi6dau6d+9um+mQl5engwcPql69eoUeFxYWpnfeeUdBQUFyceFXAAAAAAC4vrBA5FUIDg7Wp59+qh07dig5OVlDhgzR8ePHL3vco48+qpMnT6pPnz766quv9NNPP+mTTz7RwIEDlZubWwyVAwAAAABw7RA2XIUpU6YoLCxMERERateunfz9/dWjR4/LHnfDDTdo+/btys3NVUREhBo2bKgRI0bI19dXTk78SgAAAAAApRtvoyilLqwCytso4Ai8jQIAAAAoe67kbRT8MzoAAAAAADAVqxOWct/FRlw2UQIAAAAAoDgxswEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJiKsAEAAAAAAJjKxdEF4Oo0nLZBTlYPR5eB60jqrChHlwAAAACglGNmAwAAAAAAMBVhAwAAAAAAMBVhAwAAAAAAMFWZCRuio6PVo0ePfO2JiYmyWCw6ffp0kcZp166dRo4ceVVjAAAAAABwPSszYUNpYBiGcnJyHF0GAAAAAABXhbDhIidOnFCfPn1UvXp1eXh4qFGjRlqxYoVtf3R0tLZs2aKFCxfKYrHIYrEoNTVV7du3lySVL19eFotF0dHRkv4JD+bMmaPatWvL3d1dTZo00dtvv20b78KMiA0bNig8PFxWq1Vbt24t1msGAAAAAMBsvPryIufPn1fz5s01YcIE+fj46MMPP1S/fv1Uu3ZttWjRQgsXLtSPP/6ohg0b6sknn5QkVa5cWe+8847uvvtu/fDDD/Lx8ZG7u7skafLkyVqzZo0WLVqkkJAQff7553rggQdUuXJltW3b1nbe8ePHa+7cuapdu7b8/PwKrC0zM1OZmZm2zxkZGdfuRgAAAAAAcBXKVNiwbt06eXl52bXl5ubafq5WrZrGjh1r+/zYY49p/fr1euutt9SiRQv5+vrK1dVVHh4e8vf3t/WrUKGCJKlKlSq2sODs2bN69tlntWnTJrVs2VKSVLt2bW3btk2LFy+2CxuefPJJderUqdDa4+LiFBsb+98uHAAAAACAYlSmwob27dtr0aJFdm1ffvmlHnjgAUn/BA+zZs3SqlWr9Msvv9hmE3h6el7xuQ4cOKDz58/nCxGysrLUrFkzu7bw8PDLjhcTE6PRo0fbPmdkZCgwMPCK6wIAAAAA4ForU2GDp6engoOD7dqOHj1q+3nevHmaP3++FixYoEaNGsnT01MjR45UVlbWFZ8rLy9PkvThhx+qWrVqdvusVmu+ui7HarXmOw4AAAAAgJKoTIUNl7N161Z1797dNtMhLy9PBw8eVL169Wx9XF1d7R69uNAm2T+SUb9+fVmtVqWlpdk9MgEAAAAAwPWOsOEiwcHBeuedd7Rjxw6VL19ezz77rI4fP24XNgQFBenLL79UamqqvLy8VKFCBdWsWVMWi0Xr1q1Tly5d5O7uLm9vb40dO1ajRo1SXl6eWrdurYyMDO3YsUNeXl568MEHHXilAAAAAABcO7z68iJTpkxRWFiYIiIi1K5dO/n7+6tHjx52fcaOHStnZ2fVr19flStXVlpamqpVq6bY2Fg98cQTqlq1qoYPHy5JmjFjhqZOnaq4uDjVq1dPERER+uCDD1SrVi0HXB0AAAAAAMXDYhiG4egicOUyMjLk6+urwJGr5WT1cHQ5uI6kzopydAkAAAAASqAL30PT09Pl4+NTaF9mNgAAAAAAAFOxZkMp911sxGUTJQAAAAAAihMzGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKkIGwAAAAAAgKlcHF0Ark7DaRvkZPVwdBko4VJnRTm6BAAAAABlCDMbAAAAAACAqQgbAAAAAACAqcpM2BAdHS2LxaKhQ4fm2zds2DBZLBZFR0cXf2EAAAAAAFxnykzYIEmBgYFauXKlzp07Z2s7f/68VqxYoRo1ajiwMgAAAAAArh9lKmwICwtTjRo1tGbNGlvbmjVrFBgYqGbNmtnaMjMz9fjjj6tKlSpyc3NT69attWvXLtv+hIQE+fn52Y29du1aWSwW2+dvv/1W7du3l7e3t3x8fNS8eXPt3r3btn/Hjh1q06aN3N3dFRgYqMcff1xnz569BlcNAAAAAEDxKlNhgyQNGDBA8fHxts/Lli3TwIED7fqMHz9e77zzjpYvX66vv/5awcHBioiI0MmTJ4t8nr59+6p69eratWuX9uzZoyeeeELlypWTJO3bt08RERHq2bOn9u7dq1WrVmnbtm0aPny4ORcJAAAAAIADlbmwoV+/ftq2bZtSU1N1+PBhbd++XQ888IBt/9mzZ7Vo0SI988wz6ty5s+rXr68lS5bI3d1dS5cuLfJ50tLS1LFjR9WtW1chISG699571aRJE0nSM888o/vvv18jR45USEiIbr31Vj333HN67bXXdP78+QLHy8zMVEZGht0GAAAAAEBJ5OLoAopbpUqVFBUVpeXLl8swDEVFRalSpUq2/SkpKcrOzlarVq1sbeXKldPNN9+s5OTkIp9n9OjReuihh/T666+rY8eOuvfee3XjjTdKkvbs2aNDhw7pzTfftPU3DEN5eXn6+eefVa9evXzjxcXFKTY29r9cMgAAAAAAxarMzWyQpIEDByohIUHLly/P9wiFYRiSZLf+woX2C21OTk62fhdkZ2fbfZ4+fbr279+vqKgobdq0SfXr19e7774rScrLy9OQIUOUlJRk27799lsdPHjQFkj8W0xMjNLT023bkSNH/vsNAAAAAADgGiqTYUNkZKSysrKUlZWliIgIu33BwcFydXXVtm3bbG3Z2dnavXu3bcZB5cqV9ddff9kt6JiUlJTvPKGhoRo1apQ++eQT9ezZ07ZWRFhYmPbv36/g4OB8m6ura4E1W61W+fj42G0AAAAAAJREZTJscHZ2VnJyspKTk+Xs7Gy3z9PTU4888ojGjRun9evX68CBAxo8eLD+/vtvDRo0SJLUokULeXh4aOLEiTp06JD+97//KSEhwTbGuXPnNHz4cCUmJtrWhdi1a5ctrJgwYYK++OILPfroo0pKStLBgwf1/vvv67HHHiu2ewAAAAAAwLVSJsMGSYXODpg1a5buvvtu9evXT2FhYTp06JA2bNig8uXLS5IqVKigN954Qx999JEaNWqkFStWaPr06bbjnZ2ddeLECfXv31+hoaHq1auXOnfubFtzoXHjxtqyZYsOHjyo2267Tc2aNdOUKVMUEBBwza8bAAAAAIBrzWL8e/EBlAoZGRny9fVV4MjVcrJ6OLoclHCps6IcXQIAAAC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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Important Characters by PageRank'\n",
    "\n",
    "pr_df.head(20).plot.barh(figsize=(12,8), title=title).invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0eaa14c1",
   "metadata": {},
   "source": [
    "# Egocentric Network Analysis\n",
    "Next, let's look at the Ego Graphs for each of the top ten characters found by PageRank. Before visualizing and inspecting the Ego Graphs, let's create a Python list so that we can more easily look through them without explicitely typing their names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f595d51",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_nodes = pr_df.head(20).index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "83a68015",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Alice'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_nodes[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096656fb",
   "metadata": {},
   "source": [
    "Now we are all set to start looking through them, in order of importance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c5fcc65",
   "metadata": {},
   "source": [
    "## Top Node Ego Networks\n",
    "Unlike last time, I am not going to drop the center node or write about findings. This is purely discovery. Remember two things:\n",
    "- The center node is known as the 'ego' node. It is the node being looked up.\n",
    "- The other nodes are known as 'alter' nodes. They are connected to the ego node, and sometimes with each other.\n",
    "\n",
    "Pay close attention to the alter nodes that link to other alter nodes. These are interesting relationships, and you can see subcommunities in the ego network. Think about what you are seeing. What does this tell you?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "502a0105",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[0]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "be5ae30b",
   "metadata": {},
   "outputs": [
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   "source": [
    "ego = top_nodes[0]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego, center=False)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d8367777",
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    "ego = top_nodes[1]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "da93b90d",
   "metadata": {},
   "outputs": [
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    "ego = top_nodes[2]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "de9805bf",
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    "ego = top_nodes[3]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2ae509d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ego = top_nodes[4]\n",
    "\n",
    "G_ego = nx.ego_graph(G, ego)\n",
    "\n",
    "draw_graph(G_ego, show_names=True, node_size=3, edge_width=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2c1e22",
   "metadata": {},
   "source": [
    "# Community Detection\n",
    "\n",
    "We have one large connected component, and this will be great for use in Community Detection. The smaller connected components are communities of their own in this network, split off from the main piece of this network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d3ab1da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_partition(G, partition): \n",
    "\n",
    "    import matplotlib.cm as cm \n",
    "    import matplotlib.pyplot as plt \n",
    "\n",
    "    # draw the graph \n",
    "    plt.figure(3,figsize=(12,12)) \n",
    "    pos = nx.spring_layout(G) \n",
    "\n",
    "    # color the nodes according to their partition \n",
    "    cmap = cm.get_cmap('jet', max(partition.values()) + 1) \n",
    "    nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=40, cmap=cmap, node_color=list(partition.values())) \n",
    "    nx.draw_networkx_edges(G, pos, alpha=0.5, width=0.3) \n",
    "\n",
    "    return plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b1176e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# identify communities; set resolution to 0.01 for smaller groups and 1 for larger groups, etc\n",
    "# play with the number to get communities to the size you want them, or leave it at the default\n",
    "\n",
    "import community as community_louvain\n",
    "\n",
    "partition = community_louvain.best_partition(G, resolution=10, random_state=1337) # 10 was good for this network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "46ce1f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\itsgo\\AppData\\Local\\Temp\\ipykernel_1368\\2878357847.py:11: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = cm.get_cmap('jet', max(partition.values()) + 1)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_partition(G, partition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b0a3811a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "community\n",
       "38    3\n",
       "9     3\n",
       "8     3\n",
       "17    3\n",
       "40    3\n",
       "2     2\n",
       "20    2\n",
       "33    2\n",
       "1     2\n",
       "29    2\n",
       "25    2\n",
       "23    2\n",
       "41    2\n",
       "18    2\n",
       "16    2\n",
       "6     2\n",
       "3     2\n",
       "4     2\n",
       "10    1\n",
       "15    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "community_df = pd.DataFrame().from_dict(partition, orient='index')\n",
    "community_df.reset_index(inplace=True)\n",
    "community_df.columns = ['node', 'community']\n",
    "\n",
    "# remove junk; identify relevant communities\n",
    "\n",
    "community_df['community'].value_counts()[0:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd6878d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38,  9,  8, 17, 40,  2, 20, 33,  1, 29, 25, 23, 41, 18, 16,  6,  3,\n",
       "        4, 10, 15], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comm_idx = community_df['community'].value_counts()[0:20].index.values\n",
    "comm_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "233c89fa",
   "metadata": {},
   "source": [
    "## Exploring Communities\n",
    "Below, I am showing the largest community in the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "89f59e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"790.0\" height=\"540\">\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 720 520 379 267\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 379 267 720 520\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 379 267 20 20\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 20 20 379 267\"/>\n",
       "<circle cx=\"720\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"379\" cy=\"267\" r=\"3.0\" style=\"fill:rgb(179, 3, 38);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(58, 76, 192);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"726\" y=\"520\" font-size=\"10\">âUp</text><text text-anchor=\"start\" x=\"385\" y=\"267\" font-size=\"10\">Hatter</text><text text-anchor=\"start\" x=\"26\" y=\"20\" font-size=\"10\">âHe</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = community_df[community_df['community']==comm_idx[0]]['node'].values\n",
    "\n",
    "G_comm = G.subgraph(nodes)\n",
    "G_net_main = G_comm.copy() # we will run community detection off of this at the end\n",
    "\n",
    "draw_graph(nx.k_core(G_comm, 1), show_names=True, node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "68fd8c6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hatter\n",
      "âHe\n",
      "âUp\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = [n for n in sorted(G_net_main.nodes)]\n",
    "[print(n) for n in nodes]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a1fb4fb",
   "metadata": {},
   "source": [
    "## More Communities!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "489ddd48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"800.0\" height=\"540\">\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 23 20 720 271\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 23 20 20 520\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 720 271 23 20\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 720 271 20 520\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 20 520 23 20\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 20 520 720 271\"/>\n",
       "<circle cx=\"23\" cy=\"20\" r=\"3.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"720\" cy=\"271\" r=\"3.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/>\n",
       "<circle cx=\"20\" cy=\"520\" r=\"3.0\" style=\"fill:rgb(219, 220, 222);stroke:black;stroke-width:1.0\"/>\n",
       "<text text-anchor=\"start\" x=\"29\" y=\"20\" font-size=\"10\">Paris</text><text text-anchor=\"start\" x=\"726\" y=\"271\" font-size=\"10\">London</text><text text-anchor=\"start\" x=\"26\" y=\"520\" font-size=\"10\">Rome</text></svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = community_df[community_df['community']==comm_idx[1]]['node'].values\n",
    "\n",
    "G_comm = G.subgraph(nodes)\n",
    "\n",
    "draw_graph(nx.k_core(G_comm, 1), show_names=True, node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "03a0995f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "London\n",
      "Paris\n",
      "Rome\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(n) for n in sorted(G_comm.nodes)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "628c3d9c",
   "metadata": {},
   "source": [
    "# Use Any Text\n",
    "\n",
    "You can do this with any text whatsoever, whether it be literature, audio transcribed into text, online news, anything. If you have text and spaCy's NER models are able to extract entities, then this will work.\n",
    "\n",
    "This in no way diminishes the value of text or of reading. It is an additional tool that can be used to understand the relationships that exist in text. It is an additional learning tool, for understanding. It is not a replacement for reading. It can give greater understanding and awareness.\n",
    "\n",
    "The first time I did this, I used the book of Genesis from the Bible.\n",
    "\n",
    "NER models will improve over time, and LLMs can extract entities as well. Use whatever you want for the entity extraction, and then use this or a similar approach for wiring the network together."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e955a4",
   "metadata": {},
   "source": [
    "# Shattering Networks\n",
    "\n",
    "We'll talk about this more later, and simulate attacks on networks. Yesterday, I wanted to revisit the idea of what would happen if the Red Queen had her way and managed to kill Alice. Below shows the original network and the resulting network after Alice's node is removed.\n",
    "\n",
    "Networks can be used for insights but also for offense and defense. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "da7deb9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"822.0250999120964\" height=\"540\">\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 519 259 298 298\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 556 172 468 176\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 556 172 555 228\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 556 172 491 235\"/>\n",
       "<path stroke-width=\"0.5\" stroke=\"gray\" d=\"M 556 172 625 58\"/>\n",
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       "<IPython.core.display.SVG object>"
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     },
     "execution_count": 39,
     "metadata": {},
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   "source": [
    "draw_graph(G, show_names=True, node_size=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "59b83f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "G.remove_node('Alice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3028d09d",
   "metadata": {},
   "outputs": [
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