twitch_analysis/Twitch analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "asfHquiBdLzz"
   },
   "source": [
    "* Updated to GDS 2.0 version\n",
    "* Link to original blog post: https://towardsdatascience.com/twitchverse-a-network-analysis-of-twitch-universe-using-neo4j-graph-data-science-d7218b4453ff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qK12vd0AcRqG"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from neo4j import GraphDatabase\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot\n",
    "\n",
    "sns.set(font_scale = 1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FnGiX9gQcRqK"
   },
   "outputs": [],
   "source": [
    "# Connect to Neo4j\n",
    "driver = GraphDatabase.driver('bolt://localhost:7687', auth=('neo4j', 'letmein'))\n",
    "\n",
    "def read_query(query):\n",
    "    with driver.session() as session:\n",
    "        result = session.run(query)\n",
    "        return pd.DataFrame([r.values() for r in result], columns=result.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SdjDBX-KcRqL"
   },
   "source": [
    "# Exploratory graph analysis\n",
    "Before digging into graph algorithms, I like first to get acquainted with the graph at hand.\n",
    "We will perform an exploratory graph analysis by executing a couple of Cypher queries to learn more about the network. All bar charts in this blog post are created with the help of the Seaborn library. I have prepared a Jupyter notebook that contains all the code to help you follow this blog post. To begin, we will retrieve the top ten streamers by all-time historical view count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8Xra4l_HcRqN",
    "outputId": "09c45a9d-cb68-4ab9-813e-850f6d1204a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'fextralife'),\n",
       " Text(1, 0, 'riotgames'),\n",
       " Text(2, 0, 'esl_csgo'),\n",
       " Text(3, 0, 'shroud'),\n",
       " Text(4, 0, 'beyondthesummit'),\n",
       " Text(5, 0, 'summit1g'),\n",
       " Text(6, 0, 'lirik'),\n",
       " Text(7, 0, 'sodapoppin'),\n",
       " Text(8, 0, 'xqcow'),\n",
       " Text(9, 0, 'imaqtpie')]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (u:Stream)\n",
    "WHERE exists(u.total_view_count)\n",
    "RETURN u.name as streamer,\n",
    "       u.total_view_count as total_view_count\n",
    "ORDER BY total_view_count DESC \n",
    "LIMIT 10;\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"streamer\", y=\"total_view_count\", data=data, color=\"blue\")\n",
    "ax.ticklabel_format(style='plain', axis='y')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZG93vcWFcRqP"
   },
   "source": [
    "Before I get attacked by Twitch domain experts, I would like to point out that we are only analyzing Twitch streamers who have live-streamed between the 7th and the 10th of May. There might be other streamers that have higher all-time view counts. The top three all-time view count streamers in our graph seem to be a team of streamers and not an individual. I am not familiar with the mentioned streams. I only know that esl_csgo streams Counter-Strike: Global Offensive pretty much 24/7. Next, we will investigate the top ten streamers with the highest follower count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "MI72PJ4TcRqP",
    "outputId": "fb870d22-1252-46fe-fee1-55fe4a1710ff"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'tfue'),\n",
       " Text(1, 0, 'shroud'),\n",
       " Text(2, 0, 'rubius'),\n",
       " Text(3, 0, 'auronplay'),\n",
       " Text(4, 0, 'pokimane'),\n",
       " Text(5, 0, 'thegrefg'),\n",
       " Text(6, 0, 'myth'),\n",
       " Text(7, 0, 'ibai'),\n",
       " Text(8, 0, 'summit1g'),\n",
       " Text(9, 0, 'nickmercs')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (u:Stream)\n",
    "WHERE u.followers IS NOT NULL\n",
    "RETURN u.name as streamer,\n",
    "       u.followers as followers\n",
    "ORDER BY followers DESC \n",
    "LIMIT 10;\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"streamer\", y=\"followers\", data=data, color=\"blue\")\n",
    "ax.ticklabel_format(style='plain', axis='y')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4AL8sSd8cRqQ"
   },
   "source": [
    "Interestingly, we get a completely different group of streamers than in the previous query. Except for Shroud, he is fourth in the all-time view count category and second in the highest follower count. Each follower receives a notification when a stream goes live. It is almost unimaginable to think that 9 million people receive a notification when Rubius goes live or almost 8 million people when Pokimane starts streaming. I am curious to learn how long the streams have existed. We will aggregate the count of streams by their user creation date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pNfk3YqIcRqR",
    "outputId": "a7b1f780-1f89-46b8-b98f-7089d72c0ee7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '2007'),\n",
       " Text(1, 0, '2008'),\n",
       " Text(2, 0, '2009'),\n",
       " Text(3, 0, '2010'),\n",
       " Text(4, 0, '2011'),\n",
       " Text(5, 0, '2012'),\n",
       " Text(6, 0, '2013'),\n",
       " Text(7, 0, '2014'),\n",
       " Text(8, 0, '2015'),\n",
       " Text(9, 0, '2016'),\n",
       " Text(10, 0, '2017'),\n",
       " Text(11, 0, '2018'),\n",
       " Text(12, 0, '2019'),\n",
       " Text(13, 0, '2020'),\n",
       " Text(14, 0, '2021')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (u:Stream)\n",
    "WHERE u.createdAt IS NOT NULL\n",
    "RETURN u.createdAt.year as year, \n",
    "      count(*) as countOfNewStreamers\n",
    "ORDER BY year;\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"year\", y=\"countOfNewStreamers\", data=data, color=\"blue\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C_METv4IcRqS"
   },
   "source": [
    "Here I need to add another disclaimer. We are aggregating by the date of when the user account was created and not when they first started streaming. Unfortunately, we don't have the date of the first stream. Judging by the results, at least some of the streamers have already a ten-year career on Twitch. Another quite shocking fact, at least to me. We can move away from the streamers and investigate which games are played by most streamers. Note that a streamer can play more than a single game, so they might be counted under multiple games. Our data was collected between Friday and Sunday, so a streamer might prefer to play Valorant on Friday and Poker on Sunday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "T8cZ-0hTcRqS",
    "outputId": "28974ea5-66b4-4c01-9319-cf4759c3c0f7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Just Chatting'),\n",
       " Text(1, 0, 'Resident Evil Village'),\n",
       " Text(2, 0, 'Grand Theft Auto V'),\n",
       " Text(3, 0, 'League of Legends'),\n",
       " Text(4, 0, 'Fortnite'),\n",
       " Text(5, 0, 'VALORANT'),\n",
       " Text(6, 0, 'Call of Duty: Warzone'),\n",
       " Text(7, 0, 'Apex Legends'),\n",
       " Text(8, 0, 'Counter-Strike: Global Offensive'),\n",
       " Text(9, 0, 'Minecraft')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (g:Game)\n",
    "RETURN g.name as game,\n",
    "       count{ (g)<--() } as number_of_streamers\n",
    "ORDER BY number_of_streamers DESC\n",
    "LIMIT 10\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"game\", y=\"number_of_streamers\", data=data, color=\"blue\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9L6t3jTgcRqT"
   },
   "source": [
    "By far, the most popular is the Just Chatting category. The cause of the Just Chatting category popularity might be due to the reason that it is used for all streams that don't play a specific video game. This might include anything from cooking shows to \"In real life\" streams, where the streamer is walking around the world with a camera in hand. Otherwise, it seems that Resident Evil Village, GTA V, and League of Legends are the most popular games by streamers.\n",
    "Most of the streamers belong to a team or two. We can investigate which teams have the highest member count along with all the games that the team members broadcast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dQiCYgRNcRqT",
    "outputId": "d2a90765-b61f-4bfd-aea9-dbeb59cca76d"
   },
   "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>team</th>\n",
       "      <th>count_of_members</th>\n",
       "      <th>members</th>\n",
       "      <th>games</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>G FUEL: The Official Energy Drink of Esports®</td>\n",
       "      <td>64</td>\n",
       "      <td>[suto, parasite, trihex, draynilla, sirlarr, a...</td>\n",
       "      <td>[Just Chatting, Call of Duty: Black Ops Cold W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PUBG Partners</td>\n",
       "      <td>46</td>\n",
       "      <td>[taezzang, codemarco, mumino, katcontii, smoro...</td>\n",
       "      <td>[Just Chatting, PLAYERUNKNOWN'S BATTLEGROUNDS,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Corsair</td>\n",
       "      <td>43</td>\n",
       "      <td>[backyardis, varsitygaming, esl_pwnz, lokenpla...</td>\n",
       "      <td>[Sea of Thieves, Resident Evil Village, Hood: ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Twitch Ambassadors</td>\n",
       "      <td>36</td>\n",
       "      <td>[unrooolie, legiqn, mery_soldier, trihex, litt...</td>\n",
       "      <td>[Call of Duty: Warzone, Resident Evil Village,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TSM</td>\n",
       "      <td>36</td>\n",
       "      <td>[kitingishard, spicalol, tweekssb, savix, tann...</td>\n",
       "      <td>[Teamfight Tactics, League of Legends, Super S...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>inSight</td>\n",
       "      <td>36</td>\n",
       "      <td>[salista_belladonna, derhauge, daannyy, rickym...</td>\n",
       "      <td>[New Pokémon Snap, Just Chatting, It Takes Two...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Team Liquid</td>\n",
       "      <td>30</td>\n",
       "      <td>[smexycake, hungrybox, scoped, stewie2k, fr0ze...</td>\n",
       "      <td>[Tom Clancy's Rainbow Six Siege, Super Smash B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Method</td>\n",
       "      <td>30</td>\n",
       "      <td>[ds_lily, purespam, coxie, therealesquire, ski...</td>\n",
       "      <td>[Path of Exile, Old School RuneScape, World of...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Blue Microphones Crew</td>\n",
       "      <td>27</td>\n",
       "      <td>[kobe0802, legiqn, silentsentry, falloutplays,...</td>\n",
       "      <td>[Call of Duty: Black Ops Cold War, Resident Ev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Fade2Karma</td>\n",
       "      <td>26</td>\n",
       "      <td>[jane, roffle, duellinksmeta, nicholena, filth...</td>\n",
       "      <td>[Teamfight Tactics, Hearthstone, Yu-Gi-Oh! Due...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            team  count_of_members  \\\n",
       "0  G FUEL: The Official Energy Drink of Esports®                64   \n",
       "1                                  PUBG Partners                46   \n",
       "2                                        Corsair                43   \n",
       "3                             Twitch Ambassadors                36   \n",
       "4                                            TSM                36   \n",
       "5                                        inSight                36   \n",
       "6                                    Team Liquid                30   \n",
       "7                                         Method                30   \n",
       "8                          Blue Microphones Crew                27   \n",
       "9                                     Fade2Karma                26   \n",
       "\n",
       "                                             members  \\\n",
       "0  [suto, parasite, trihex, draynilla, sirlarr, a...   \n",
       "1  [taezzang, codemarco, mumino, katcontii, smoro...   \n",
       "2  [backyardis, varsitygaming, esl_pwnz, lokenpla...   \n",
       "3  [unrooolie, legiqn, mery_soldier, trihex, litt...   \n",
       "4  [kitingishard, spicalol, tweekssb, savix, tann...   \n",
       "5  [salista_belladonna, derhauge, daannyy, rickym...   \n",
       "6  [smexycake, hungrybox, scoped, stewie2k, fr0ze...   \n",
       "7  [ds_lily, purespam, coxie, therealesquire, ski...   \n",
       "8  [kobe0802, legiqn, silentsentry, falloutplays,...   \n",
       "9  [jane, roffle, duellinksmeta, nicholena, filth...   \n",
       "\n",
       "                                               games  \n",
       "0  [Just Chatting, Call of Duty: Black Ops Cold W...  \n",
       "1  [Just Chatting, PLAYERUNKNOWN'S BATTLEGROUNDS,...  \n",
       "2  [Sea of Thieves, Resident Evil Village, Hood: ...  \n",
       "3  [Call of Duty: Warzone, Resident Evil Village,...  \n",
       "4  [Teamfight Tactics, League of Legends, Super S...  \n",
       "5  [New Pokémon Snap, Just Chatting, It Takes Two...  \n",
       "6  [Tom Clancy's Rainbow Six Siege, Super Smash B...  \n",
       "7  [Path of Exile, Old School RuneScape, World of...  \n",
       "8  [Call of Duty: Black Ops Cold War, Resident Ev...  \n",
       "9  [Teamfight Tactics, Hearthstone, Yu-Gi-Oh! Due...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "MATCH (t:Team)\n",
    "WITH t, count{ (t)<--() } as count_of_members\n",
    "ORDER BY count_of_members DESC LIMIT 10\n",
    "MATCH (t)<-[:HAS_TEAM]-(member)-[:PLAYS]->(game)\n",
    "RETURN t.name as team,\n",
    "       count_of_members,\n",
    "       collect(distinct member.name) as members, \n",
    "       collect(distinct game.name) as games\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gbk0WLeKcRqU"
   },
   "source": [
    "G Fuel team had 64 members stream on the weekend between the 7th and 10th of May 2021. Its members cover 34 different game categories on Twitch. Before we dive into network analysis, let's examine the users that are VIPs of the most streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Y2oNHgLXcRqU",
    "outputId": "331fb598-aa29-4d4c-9037-1af5385de980"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'nightbot'),\n",
       " Text(1, 0, 'karuzo1g'),\n",
       " Text(2, 0, 'kristoferyee'),\n",
       " Text(3, 0, 'crazyslick'),\n",
       " Text(4, 0, 'wolfabelle'),\n",
       " Text(5, 0, 'streamelements'),\n",
       " Text(6, 0, 'supibot'),\n",
       " Text(7, 0, 'kephlivestream'),\n",
       " Text(8, 0, 'letshugotv'),\n",
       " Text(9, 0, 'saiiren')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (u:User)\n",
    "RETURN u.name as user, count{ (u)-[:VIP]->() } as number_of_vips\n",
    "ORDER BY number_of_vips DESC LIMIT 10;\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"user\", y=\"number_of_vips\", data=data, color=\"blue\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2STaGmaPcRqV"
   },
   "source": [
    "I would venture that Nightbot and Supibot are both bots. Having bots as VIPs seems a bit weird. I always thought that bots were moderators of the chat with the ability to remove messages with links and such. We can also examine which users are moderating for the highest count of streams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pUzzNm__cRqV",
    "outputId": "1746c089-e71d-4d99-894e-df703c2f9583"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'nightbot'),\n",
       " Text(1, 0, 'streamelements'),\n",
       " Text(2, 0, 'moobot'),\n",
       " Text(3, 0, 'streamlabs'),\n",
       " Text(4, 0, 'ssakdook'),\n",
       " Text(5, 0, 'wizebot'),\n",
       " Text(6, 0, 'fossabot'),\n",
       " Text(7, 0, '9kmmrbot'),\n",
       " Text(8, 0, 'priestbot'),\n",
       " Text(9, 0, 'soundalerts')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "MATCH (u:User)\n",
    "RETURN u.name as user, count{ (u)-[:MODERATOR]->() } as number_of_mods\n",
    "ORDER BY number_of_mods DESC LIMIT 10;\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"user\", y=\"number_of_mods\", data=data, color=\"blue\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sDGT5qSFcRqW"
   },
   "source": [
    "As expected, all top ten users with the highest count of moderator roles are most likely bots, with Nightbot being the most popular.\n",
    "# User network analysis\n",
    "You might remember that only around 6000 out of 10 million users in our graph are streamers. If we put that into perspective, only 0.06% of users are streamers. The other users in our database are users who have chatted on other streamers' broadcasts. So far, we have entirely ignored them in our exploratory graph analysis. That is, until now. Graph data science focuses on analyzing connections between entities in a network.\n",
    "The time is right to put on our graph data science hats and examine relationships between users in the network. As a brief reminder of the graph structure, let's visualize a small subgraph of the user network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I8hRMEwpcRqW",
    "outputId": "44e9c8eb-cc81-4f0f-dc11-66d2e9bdf394"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "      p\n",
       "0  (())\n",
       "1  (())\n",
       "2  (())\n",
       "3  (())\n",
       "4  (())\n",
       "5  (())\n",
       "6  (())\n",
       "7  (())\n",
       "8  (())\n",
       "9  (())"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "MATCH (s:Stream)\n",
    "WITH s LIMIT 1\n",
    "CALL {\n",
    "    WITH s\n",
    "    MATCH p=(s)--(:Stream)\n",
    "    RETURN p\n",
    "    LIMIT 5\n",
    "\n",
    "    UNION\n",
    "\n",
    "    WITH s\n",
    "    MATCH p=(s)--(:User)\n",
    "    RETURN p\n",
    "    LIMIT 5\n",
    "}\n",
    "\n",
    "RETURN p\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19SvDTpUcRqX"
   },
   "source": [
    "As mentioned in the previous blog post, streamers behave like regular users. They can moderate other broadcasts, can engage in their chat, or earn VIP status. This was the reason that drove the graph model decision to represent streamers as regular users with a secondary label Stream. We don't want to have two nodes in the graph represent a single real-world entity.\n",
    "To start, we will evaluate the node degree distribution. Node degree is simply the count of relationships each node has. Here, we are dealing with a directed network as the relationship direction holds semantic value. If Aimful is a moderator of the Itsbigchase stream, it doesn't automatically mean that then Itsbigchase is a moderator of the Aimful stream. When dealing with directed networks, you can split the node degree distribution into in-degree, where you count incoming relationships, and out-degree, where you are counting outgoing connections. First, we will examine the out-degree distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8tUbJrHFcRqX",
    "outputId": "622f64b3-8cf5-4f31-a4a7-25019fff70b2"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"MATCH (u:User)\n",
    "WITH u, count{ (u)-[:CHATTER|VIP|MODERATOR]->() } as node_outdegree\n",
    "RETURN node_outdegree, count(*) as count_of_users\n",
    "ORDER BY node_outdegree ASC\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.lineplot(x=\"node_outdegree\", y=\"count_of_users\", data=data, color=\"blue\")\n",
    "ax.set_yscale(\"log\")\n",
    "ax.set_xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "W8IauNbGcRqY"
   },
   "source": [
    "A log-log plot visualizes the out-degree distribution. Both axes have a logarithmic scale. Around six million out of a total of ten million users have exactly a single outgoing relationship, meaning they have only chatted in a single stream. The vast majority of users have less than ten outgoing connections. A couple of users have more than 100 outgoing links, but I would venture a guess that they are most likely bots. Some mathematicians might consider the out-degree following a Power-Law distribution. Now, let's look at the in-degree distribution. We already know that only streamers will have an in-degree higher than 0. Only the users who broadcast their streams can have users engage in their chat. Consequently, around 99.999% of users have an in-degree value of 0. You could almost treat this network as a bipartite graph, except that there are also relationships between streamers. We will visualize the in-degree distribution only for streamers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GPgErjRNcRqY",
    "outputId": "2e084956-7cec-44ea-eb8f-15b6941602ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"MATCH (u:Stream)\n",
    "RETURN u, count{ (u)<-[:CHATTER|VIP|MODERATOR]-() } as node_indegree\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.histplot(x=\"node_indegree\",data=data, color=\"blue\", binrange=[0,20000])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "n4CgrVGtcRqY"
   },
   "source": [
    "It is pretty interesting to observe that the in-degree distribution also follows the Power-law distribution. Most streamers have less than 1000 active chatters on the weekend, while some streamers have more than 15000 active chatters.\n",
    "\n",
    "# Graph Data Science library\n",
    "\n",
    "The GDS library executes graph algorithms on a specialized in-memory graph format to improve the performance and scale of graph algorithms. Using native or cypher projections, we can project the stored graph in our database to the in-memory graph format. Before we can execute any graph algorithms, we have to project a view of our stored graph to in-memory graph format. We can filter which parts of the graph we want to project. There is no need to add nodes and relationships that will not be used as an input to a graph algorithm. We will begin by projecting all User and Stream nodes and the possible relationships between them, which are CHATTER, MODERATOR, and VIP.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VgfZAtk0cRqZ"
   },
   "outputs": [],
   "source": [
    "data = read_query(\"\"\"\n",
    "CALL gds.graph.project('twitch', ['User', 'Stream'], ['CHATTER', 'VIP', 'MODERATOR'])\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OjeNrnNicRqZ"
   },
   "source": [
    "## Weakly Connected Components\n",
    "The Weakly connected components algorithm (WCC) is used to find disparate islands or components of nodes within a given network. A node can reach all the other nodes in the same component when you disregard the relationship direction. \n",
    "Use the following Cypher query to execute a Weakly-Connected Components algorithm on the Twitch network we have previously projected in memory. The stats method of the algorithm is used when we are interested in only high-level statistics of algorithm results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VWNX0QO3cRqZ",
    "outputId": "ea8263a8-d090-479a-81d5-8f3319fc7b71"
   },
   "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>componentCount</th>\n",
       "      <th>componentDistribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>{'p99': 10514239, 'min': 10514176, 'max': 1051...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   componentCount                              componentDistribution\n",
       "0               1  {'p99': 10514239, 'min': 10514176, 'max': 1051..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.wcc.stats('twitch')\n",
    "YIELD componentCount, componentDistribution\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UY4MwnwXcRqa"
   },
   "source": [
    "Very interesting to see that the user network is composed of only a single connected component. For example, you could find an undirected path between a user who watches Japanese streams and another user who looks at Hungarian streams. It might be interesting to remove the bots from the graph and rerun the WCC algorithm. I have a hunch that the Nightbot and other bots help connect disparate parts of the network into a single connected component.\n",
    "With the current in-memory graph projection, we can also filter at algorithm execution time nodes or relationships. In the next example, I have chosen to consider only Stream nodes and connections between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "t3Q9EpHHcRqa",
    "outputId": "75f719f9-1194-48fb-a340-8c1d205138e7"
   },
   "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>componentCount</th>\n",
       "      <th>componentDistribution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1902</td>\n",
       "      <td>{'p99': 3, 'min': 1, 'max': 3692, 'mean': 3.04...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   componentCount                              componentDistribution\n",
       "0            1902  {'p99': 3, 'min': 1, 'max': 3692, 'mean': 3.04..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.wcc.stats('twitch', {nodeLabels:['Stream']})\n",
    "YIELD componentCount, componentDistribution\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Mn-NMINcRqa"
   },
   "source": [
    "With this variation of the WCC algorithm, we are effectively looking at chat communication between streamers. Only Stream nodes are considered, and so, only relationships between Stream nodes are used as an input to the WCC algorithm. There are a total of 1902 separate components in the streamer network. The largest component contains around 65% of all stream nodes. And then, we are dealing with primarily single node components, where a streamer hasn't chatted in other streamers' broadcast on the specific weekend the data was scraped.\n",
    "## PageRank\n",
    "PageRank is probably one of the most famous graph algorithms. It is used to calculate node importance by considering the inbound relationships of a node as well as the importance of the nodes linking to it. PageRank was initially used to calculate the importance of websites by Google, but it can be used in many different scenarios.\n",
    "Use the following Cypher query to execute the PageRank algorithm on the whole user network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "w0jnnXJYcRqb",
    "outputId": "d2b60b25-e1ec-41e7-b05b-d673db9ede36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'yassuo'),\n",
       " Text(1, 0, 'trainwreckstv'),\n",
       " Text(2, 0, 'loltyler1'),\n",
       " Text(3, 0, 'riotgames'),\n",
       " Text(4, 0, 'xqcow'),\n",
       " Text(5, 0, 'csgomc_ru'),\n",
       " Text(6, 0, 'benjyfishy'),\n",
       " Text(7, 0, 'alexby11'),\n",
       " Text(8, 0, 'nickmercs'),\n",
       " Text(9, 0, 'esl_csgo')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yKyYmvfcCAAAgCc3dAQAAAABwCUI6AAAAAAAuQUgHAAAAAMAlCOkAAAAAALgEIR0AAAAAAJe4KUP6pEmT5PF41KBBgxTrfv75Z7Vs2VJFixZVWFiYhgwZoosXL6YoFxcXp5EjR6pcuXIKCgpSs2bNtG7dulR/XlpsEwAAAACAK910IT0qKkrvvfeebr311hTrIiMj1a5dO8XGxqpPnz5q2rSpZs6cqe7du6co26dPH02dOlX169dXv3795O/vr44dO2rTpk1pvk0AAAAAAFJz082TPmrUKBUpUkRmprNnz/qsGz16tO666y59/PHHuu222yRJDz30kPr3769169YpNDRUkrR161YtWrRIffv2Vbt27SRJDRs2VN26dRUREaFPP/00TbcJAAAAAEBqbqo36Vu3btXChQvVt2/fFOtiYmK0du1aNWzY0AnTktSgQQPdeuut+uqrr5xlS5YsUbZs2fTEE084y3LkyKGmTZtq48aNOnHiRJptEwAAAACAq7lpQrqZafDgwWrYsKEKFSqUYv2uXbt0+fJlFSlSxGd59uzZVahQIUVGRjrLIiMjVaBAAZ/gLUlBQUEyM6dsWmwTAAAAAICruWmau8+fP1979uzR+PHjU10fFRUlScqTJ0+KdXny5NHmzZt9yubNmzfVcpKct95psc1r8a9/5brmz+BPefLcnt67kOFRx2mPOr4xrkc9x8YmKkeOm+bZ9w1F3QAA8PfdFCE9JiZGo0aNUqdOnXTvvfemWubSpUuSkt5yXylHjhzOem/ZbNmypVpOkmJjY9Nsm9fi5MkYJSbaVddz8/7XoqLO/eNtUMd/jTpOe9TxjXG96rlAgf3/fGcyoH378l+XOgYAICPw9/f7yxeyN8Vj7ffee0/ZsmXT008/fdUyOXPmlJQ0DdqVYmNjnfXesvHx8amWk/4M1mmxTQAAAAAArsb1b9JPnDihqVOnqlu3bvrjjz+c5bGxsYqPj9fhw4d1++23O83KvU3Uk4uKivJ5A58nT55Um597P+stmxbbBAAAAADgalz/Jv3kyZOKj49XRESEqlSp4vxvy5Yt2rt3r6pUqaJJkybp8ccfV9asWbVt2zafz8fFxSkyMtJnsLmCBQtq3759On/+vE/ZLVu2OOslpck2AQAAAAC4GteH9Iceekjjx49P8b/HHntMDz74oMaPH6+GDRvq9ttvV2hoqBYsWOATlBcsWKALFy6oZs2azrKaNWsqPj5es2fPdpbFxcVp7ty5Kl68uDMAXFpsEwAAAACAq3F9c/fbb79dVatWTbF86tSpypIli8+67t27q0WLFmrTpo2eeOIJHTt2TFOmTFGFChVUtmxZp1zRokVVs2ZNRUREKCoqSg8//LDmzZunI0eOaOjQoT4/Jy22CQAAAABAalwf0q9FQECApkyZooiICA0dOlS5cuVSs2bN1KNHjxRlR4wYoTFjxmjBggWKjo6Wx+PRxIkTVaJEiTTfJgAAAAAAqfEzs6vP8YV09XemYGO6n9Rdr+l+qOOro47THnV8Y1DPaY8p2AAA+FOGmIINAAAAAIDMgJAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCUI6QAAAAAAuAQhHQAAAAAAlyCkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCUI6QAAAAAAuAQhHQAAAAAAlyCkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCUI6QAAAAAAuAQhHQAAAAAAlyCkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCUI6QAAAAAAuAQhHQAAAAAAlyCkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCWypvcOAAAA/B25ct2qW27Jkt674UoXLyYoJuZCeu8GAOA6IKQDAICbwi23ZFGBAvvTezdcad++/IqJSe+9AABcDzR3BwAAAADAJQjpAAAAAAC4BM3dAQAAIIl+//8Lff8B3AiEdAAAAEii3///Qt9/ADcCzd0BAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCUI6QAAAAAAuAQhHQAAAAAAlyCkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoAAAAAAC5BSAcAAAAAwCWypvcOAAAAAJlFrly36pZbsqT3brjWxYsJiom5kN67AaQrQjoAAABwg9xySxYVKLA/vXfDtfbty6+YmPTeCyB90dwdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEswcBwAAACADIVR9K+OEfTdj5AOAAAAIENhFP2rYwR996O5OwAAAAAALkFIBwAAAADAJQjpAAAAAAC4BH3SAQAAAADXhMH5ru6fDs5HSAcAAAAAXBMG57u6fzo4H83dAQAAAABwCUI6AAAAAAAuQUgHAAAAAMAlCOkAAAAAALgEIR0AAAAAAJcgpAMAAAAA4BKEdAAAAAAAXIKQDgAAAACASxDSAQAAAABwCUI6AAAAAAAuQUgHAAAAAMAlboqQvnXrVg0aNEi1a9dWcHCwKlWqpO7du+vAgQMpyv78889q2bKlihYtqrCwMA0ZMkQXL15MUS4uLk4jR45UuXLlFBQUpGbNmmndunWp/vy02CYAAAAAAFe6KUL65MmTtWzZMpUtW1b9+vVTs2bN9OOPP6phw4bau3evUy4yMlLt2rVTbGys+vTpo6ZNm2rmzJnq3r17im326dNHU6dOVf369dWvXz/5+/urY8eO2rRpk0+5tNgmAAAAAACpyZreO/B3tGvXThEREcqePbuzrHbt2qpXr54mTZqkYcOGSZJGjx6tu+66Sx9//LFuu+02SdJDDz2k/v37a926dQoNDZWU9GZ+0aJF6tu3r9q1aydJatiwoerWrauIiAh9+umnzs9Ji20CAAAAAJCam+JNevHixX0CuiTlz59fjz32mPMmPSYmRmvXrlXDhg2dMC1JDRo00K233qqvvvrKWbZkyRJly5ZNTzzxhLMsR44catq0qTZu3KgTJ06k2TYBAAAAALiamyKkp8bM9Mcff+juu++WJO3atUuXL19WkSJFfMplz55dhQoVUmRkpLMsMjJSBQoU8AnekhQUFCQzc8qmxTYBAAAAALiamzakL1y4UMePH1etWrUkSVFRUZKkPHnypCibJ08enzfZUVFRuvfee1MtJ8kpmxbbBAAAAADgam6KPulX2rt3r15//XWVKFFCDRo0kCRdunRJklI0i5eSmp1713vLZsuWLdVykhQbG5tm27wW//pXrmv+DP6UJ8/t6b0LGR51nPao4xuDek571HHao45vDOo57VHHaY86Tnv/pI5vupAeFRWlzp07684779Tbb78tf/+kxgA5c+aUlDQN2pViY2Od9d6y8fHxqZaT/gzWabHNa3HyZIwSE+2q6/nj+mtRUef+8Tao479GHac96vjGoJ7THnWc9qjjG+Of1jN1/L9xLKc96jjt/VUd+/v7/eUL2ZsqpJ87d04dO3bUuXPnNGPGDJ9m6N7/722intyVTdGvbKqevJwkp2xabBMAAAAAgKu5afqkx8bG6tlnn9X+/fs1YcIEPfrooz7rH3/8cWXNmlXbtm3zWR4XF6fIyEgVKlTIWVawYEHt27dP58+f9ym7ZcsWZ31abRMAAAAAgKu5KUJ6QkKCXnrpJW3evFlvv/22goODU5S5/fbbFRoaqgULFvgE5QULFujChQuqWbOms6xmzZqKj4/X7NmznWVxcXGaO3euihcvrrx586bZNgEAAAAAuJqborn7sGHDtGLFCoWHh+vMmTNasGCBs+62225T1apVJUndu3dXixYt1KZNGz3xxBM6duyYpkyZogoVKqhs2bLOZ4oWLaqaNWsqIiJCUVFRevjhhzVv3jwdOXJEQ4cO9fnZabFNAAAAAABSc1OE9J07d0qSVq5cqZUrV/qse/DBB52QHhAQoClTpigiIkJDhw5Vrly51KxZM/Xo0SPFNkeMGKExY8ZowYIFio6Olsfj0cSJE1WiRAmfcmmxTQAAAAAAUnNThPSPP/74b5ctWbKkPvvss/9ZLkeOHHrllVf0yiuvpMs2AQAAAAC40k3RJx0AAAAAgMyAkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCEdAAAAAACXIKQDAAAAAOAShHQAAAAAAFyCkA4AAAAAgEsQ0gEAAAAAcAlCOgAAAAAALkFIBwAAAADAJQjpAAAAAAC4BCH9OouLi9PIkSNVrlw5BQUFqVmzZlq3bl167xYAAAAA4CZASL/O+vTpo6lTp6p+/frq16+f/P391bFjR23atCm9dw0AAAAA4HKE9Oto69atWrRokXr16qXevXurefPmmjp1qu6//35FRESk9+4BAAAAAFyOkH4dLVmyRNmyZdMTTzzhLMuRI4eaNm2qjRs36sSJE+m4dwAAAAAAtyOkX0eRkZEqUKCAbrvtNp/lQUFBMjNFRkam054BAAAAAG4GWdN7BzKSqKgo5c2bN8XyPHnySNI1v0n39/f7n2UefJBf4dX8nfr7O6jjq6OO0x51fGNQz2mPOk571PGNcT3qmTr+axzLaY86Tnt/Vcf/q/79zMyu9w5lVlWrVtV//vMfvf/++z7LDx06pKpVq2rAgAFq3bp1Ou0dAAAAAMDtaO5+HeXMmVPx8fEplsfGxkpK6p8OAAAAAMDVENKvozx58qTapD0qKkqSdO+9997oXQIAAAAA3EQI6ddRwYIFtW/fPp0/f95n+ZYtW5z1AAAAAABcDSH9OqpZs6bi4+M1e/ZsZ1lcXJzmzp2r4sWLpzqoHAAAAAAAXgzHdx0VLVpUNWvWVEREhKKiovTwww9r3rx5OnLkiIYOHZreuwcAAAAAcDlGd7/OYmNjNWbMGH3xxReKjo6Wx+NRjx49VLZs2fTeNQAAAACAyxHSAQAAAABwCfqkAwAAAADgEoR0AAAAAABcgpAOAAAAAIBLENIBAAAAAHAJQjoApJGEhIT03gUAANIF10Dg/46QDiDNZbZJJLzfN0uWLM6yxMTE9Nod/H/8DgBcC84Z/zfJr4GJiYmaP3++4uPj03mvgJsLIR2ultnCXUYyb948zZ07V5Lk5+eXzntzY3m/79dff62yZctq586d8vf3561COvHWu78/l7xrtW7dOkmci93sxIkT6b0LGZa/v78T1Pkb+Pu818DvvvtOVapUUZ8+fbRo0aJ03qvMJ7V7Do7j/7sbXXfcscA14uLiNH/+fC1fvlxbt26VlPnCXUYyaNAg/fzzz5IyX5O3s2fP6q233tLLL7+sU6dOadiwYZJ836zjxkhMTHTqfePGjRo7dqz27duXznvlfmamTz/9VE8//bR27dolPz8/bu5cqGfPnnruuec4ptPI8uXL1apVK0ncj1yLxMRErVixQgMHDpSfn58eeeQRjRs3TtHR0em9a5mCmcnMnGvf999/r2XLlsnMOI7/D7z3sFfWXVpfE7Om6daBv2nu3LkaPny4/P39FR0dLX9/fz3zzDNq3ry57r//fk4s6exq9X/58mVlzZryNBIXF6fy5ctrx44dkjJXOI2Pj9fy5cs1YcIEhYaGKl++fJo1a5a++uor1apV66p1hrTh7++vY8eOaeDAgdqwYYNuu+02Xb58WS+99BLnlL/g5+ene+65R/fdd5927twpj8dDfblIYmKi/P39Va5cOS1atEjffvutHnnkEVqLXGc//fST9u/fr3379qlAgQLpvTs3jcuXL+uXX37R2bNnFRERofj4ePXs2VMffvihunfvnt67l+F5z9WbNm3S0KFDdfjwYT3wwAPKkSOHKlSokM57d3NJ/rBj9erV2rdvnx588EGFhITorrvucsqkxfWRsznS3Zo1azRq1CiVL19eo0aN0vvvv68mTZro/fff13vvvae4uDhuDtPZlfV/+fJlSXLC5pEjRyT92X8ve/bsuueeexQdHa29e/fewD1Nf9myZVNCQoJat26tKVOm6Pnnn1eRIkWct+lZs2bljeQNtHfvXnXs2FGnT5/WgAEDNGHCBHXv3p1zSjLe43HQoEEaNGiQs7xGjRqKj4/XL7/84lMO6efcuXOS/jwnN2rUSMHBwfrss8+0bdu29Ny1m1Zq/c69b84CAwMVGxurS5cu3ejdcr2/Oh9kz55dderU0ddff62KFSuqePHiql27tj766KNMd0+QXj7//HN17NhRuXPnVu/evTVo0CAC+v+Bn5+fDh48qCeffFIvvvii3nvvPXXt2lWtW7fWl19+6ZRJC4R0pBvvhXHFihWKj49Xt27dVLZsWVWoUEGDBg1SnTp1tGTJEi1YsEASN4jpKTo6Wp988ol27twp6c9wHhsbq8qVK+u5557T/v37fd7ilC9fXr///rvze8vov7/ExETn4UWdOnXUr18/SdJ9992nJk2a6Pjx4xo3bpxTFtfX1er022+/1cmTJ9WlSxfVrVtXhQoVusF75n5+fn46cuSIZsyYoRkzZmjy5Mk6dOiQJKlmzZr65ptvnHK48cxMMTExql+/viZPnqxLly7Jz8/PGYhr4MCB+u2337RkyRKdP38+nff25uPtd75q1aoUzVoLFSqkxMRE59rHuTtJ8jeHP//8s+Li4pzlXv/5z3+UO3duJSYmKnfu3GrYsKFuv/12vfPOO+myzxlVat0JY2Ji9Pnnn6ts2bLq06eP6tevr4CAgHTYu5vfxYsXNWbMGB09elRDhw7V5MmTNXHiRF26dEkjRoxwro9pcW4gpOOGW7lypaSkC+OlS5e0fft25cuXTw8++KAkOSf7bt26KUeOHFqyZInOnDnDDWI6OnPmjL788kunmdrWrVsVHh6ugwcP6sUXX1RcXJyef/55bdiwwfnMfffdp1y5cunbb79Nr92+ofz9/ZU1a1YdOnRIiYmJPjfRlStXVpUqVfTee+/p1KlTypIlS6brp59WzEwJCQlXbeZ74MAB5c6dWxUqVFDWrFl14MABrVmzRsuWLdO6deucBysZ/SFSak6ePOncWNxzzz2qVauWbr31Vq1Zs0bTpk2TJIWGhurUqVNpeiOCqzt06JDmzJkjSXr00Ue1cOFC7d69W9KfrXYKFSqkhg0bas6cOT7nYCS5cmC9K4/huLg4jR8/Xs8++6yGDx+uqKgo53zi7++vPHnyOF236E6QxPt28emnn1arVq00ffr0vywrJbVKaN68ub7++utMc1+QlszMZ8yVXbt26fjx4zIzXbp0ScePH1eRIkWUP39++fv767ffftPOnTu1efNmRUVFpfPe3zwOHDigr7/+Wo0bN1bt2rUVGBioChUqaNiwYcqWLZveffddxcfHp8m5gbMNbpiEhARNmjRJzz33nDMwXM6cOfXwww8rJiZGFy5ckJTUTCoxMVEPP/ywqlevrm3btnFCSWePPPKI6tWrp3379umJJ55QixYtdPfdd+v8+fNq2LChRo4cqUuXLqlfv36aP3++JOnuu+9W7ty5tW/fPsXHx2fIhyzJb/YSExP15ptv6qmnntKKFSskJd1ES1LevHnVqFEj5cyZU0OGDJHEW8nrwfs2J0uWLPrjjz/07rvvatq0ac4AOVJSq4Y9e/aoXbt2atmypZ5++mn169dPXbt21dNPP62IiAidP38+0/0+Jk2apMaNG2vhwoWSks7PBQoUUGhoqIoVK6b169dr3Lhx8ng8uu+++/Tzzz/LzAgpN9DGjRtVo0YNLVmyRMePH1fPnj0VHR2tuXPnOgNweY/zvn376uLFi1q4cKGOHz+enrvtGnFxcWrWrJn69++vkydPOsu8x7D3QWn27NnVpUsXPffcc/riiy+cAT8lqUCBAsqVK5cT9HlIleTnn3/WK6+8os2bNyt37txasmSJjh49Kj8/vxR15D233nrrrapWrZoKFSqkMWPGKC4uLlM+HL1e/Pz85O/vr8jISLVr107PPvus3n77bR0+fFj+/v4KCAjQZ599pnfffVcdOnTQSy+9pDZt2ujJJ5/Uc889p2PHjqX3V3CFxMREn2N27dq1zoNQSTp48KASEhL06KOPSvrzvFGyZEnVrVtXv/32W5q1+OVqizSV/IDNkiWLypcvL4/H4zR3MjOVKVNGBw4c0KpVq5xl3j+C4OBgnTt3jreO6cB70vL+9+zZs5KkyMhIvfbaa/rggw9UpEgRSVKRIkU0fvx4PfDAA+rfv78WLFig+++/X48//rh+/fVXZcuWLUPe3Hhv9jZv3qzjx49r7dq1atmypQoXLuyU8f4NhIaGqmHDhlq8eLE2bdpE2LkOvDd/H3zwgapUqaLZs2dr/Pjx6tq1q3r06KE9e/aoZMmSeuONN5QlSxbdcccdaty4sXr16qXvvvtO9evX14IFC3Tw4MF0/iY3Xnh4uB566CFFRERo69atuuWWW5QvXz6dOHFC5cuXV8eOHTVu3DhFRkYqZ86c2rt3LyMz3yCLFi3S22+/rQULFqhx48Z6+eWX9fDDDytfvnzq0KGD5s+fr02bNklK6noUFxenO++8U9WqVdN3332n77//Pp2/QfozM2XPnl0ej0fr1q1z3txmz55dUVFR+u9//6tBgwY5rRT8/Pz0wgsv6JVXXlFkZKR69OjhfKZ06dLOTCWct5NCyhdffKGtW7dqwIABat++vfbu3asZM2ZI+us6evzxx9WiRQtt375ds2fPds7hp0+fviH7nlEkJibKzDRnzhw9+eSTSkxMVJs2bVS7dm3ly5dPuXPn1lNPPaWgoCB99NFHkpLuQfr166d+/fppz549zvLM/KDE2wrPO2j18ePH9eKLL/rMROAN56dOnVJCQoJPS8imTZtKSrovvnz58vV/2G9AGjl58qTt3LnTzMxiY2PNzCw+Pt4+++wz83g8tmjRIjMz2717tzVv3tyqVKliR44c8dlGnz59rESJErZv3z5LTEy8sV8gk0pISLCEhATn3+fOnTMzs3HjxlnXrl2tUKFCNnjwYDMzS0xM9Pm9HDx40Lp27WrBwcE2cuRI++STT6xkyZIWFRXllM9IEhIS7J133jGPx2N169a1fv36WVxcXIpy3u+9YcMGq1q1qjVu3NjMzE6fPm0bNmywP/74w6cc/r5169ZZeHi4vfnmm7Zt2zaLjo62efPmWbly5axdu3ZOueTnIK+FCxeax+OxHTt23PD9vtG2b99ukZGRdvLkSecY/emnn6xFixYWHh5u27dvt4SEBAsJCbG5c+eamdmIESOsefPmVr16dStZsqQdP37czDhO01rdunUtNDTUwsLC7IcffvBZFxMTY1WrVrVOnTrZ77//7rPu5ZdfNo/HY61atXKuvZmV9xp2+fJlK1WqlLVq1cr27dtnv/zyi4WEhFiFChUsLCzMPB6PDRkyxI4ePep8dv369Va7dm0rU6aMbd261d59910LDw+3DRs2pNfXcQ1vva5du9bWr19vZknng5YtW1q1atVs8+bNZpZU71fynjcOHjxozz77rIWFhdnu3bvtyy+/tF69etnGjRtv0Le4+SQ/53p/B2fPnrXGjRtbmzZtbPfu3T73bcnLxsbG2qVLl5zz/vnz56158+bWrFmzG7PzLhcbG2sDBw60Ro0aWefOnS08PNyKFy9uK1eutISEBDt37py1aNHCmjRp4nOv5v2d1K1b17p37+4sv554JIg0cerUKXXp0sWZX9TbhD1r1qwqX768wsLC9Pbbbys2NlaPPfaYnnrqKZ07d07dunXT8uXLtWvXLn3++ef64YcfVKtWLeXPnz/TNUdND95pffz9/bV371716dNHQ4YM0caNG/XCCy/o1VdfVd26dfXJJ5/owIEDKZq25cuXT2PGjFG1atU0Y8YMTZo0Sf7+/hli3ntv3+Xk/P39FRQUpCJFiujgwYMKCQlRtmzZrvpkukSJEs5bhLfeekvjxo1T9+7dtWjRIkk3d/2kJfv/c76mZtasWcqRI4eeffZZBQQE6I477lDevHl19uxZHThwQLt27ZKUdA6Skt48XrhwQZs3b9bMmTNVrlw55cuX74Z9lxttz549atWqlVq3bq2WLVuqZcuWev/99yUlNdcbPny4zEz9+/fX1q1b1aJFC3366aeSpJdfflmlS5eWlDSquLcrC8dp2vCeY1577TVdvHhR586d02OPPeaz7rbbblP37t21evVqZ2RhSVq3bp3OnDmjdu3aaffu3Zlq2svU+Pv76/Lly8qSJYt69eqljRs3atmyZZo9e7aqVKmiDz74QAsXLlSXLl00a9YsLV682PlsqVKlNGLECHk8HvXp00c7duzQyZMnnTfEVzsXZXTJx/4IDQ1VqVKlnK5s7dq109mzZ51zR2rHn/e8kS9fPtWrV0+XLl1Sly5d1Lt3b23atMk5R+NP3vsrb92tXLlSw4cP18WLF/XTTz9p+/btatSokR577DH5+/unODb9/PyUPXt25ciRQ9myZdPZs2e1evVqHT58WPXr17/h38dtYmJi9Oyzz2rJkiWqUqWKwsLCVLp0aZ0/f14ffPCBzpw5o9tuu01PPPGEduzYoc8//1zR0dHy8/OTn5+fNmzYoP379ytv3rxpsn+EdFxX3r7j3pE84+LinCY13uYhDzzwgJ588kkdPXpUH3zwgSSpWrVqeuONN/T777+rS5cuateunQYOHCiPx6Nu3bqly3fJjPz9/RUfH6833nhDjRo10u7du5UtWzZnALT77rtPdevWVe7cuTV48GDnM94Lg/ci3qtXL3Xr1k3Hjh1TXFxcmp3AbgTvRdI7ov3evXt15swZZ33FihUVGhqq2NhYp4/Xld0zkoeaRx99VA888IAmTJig6dOnq0GDBnrqqafS+FvcvBISEpwL4pViYmK0f/9+Va9eXXfffbf279+vjh076umnn1bVqlX11ltvKX/+/JKSbqxPnTqlDz/8UBMmTFCvXr105MgRPfXUU8qVK9cN/lY3xp49e9SlSxclJCSoR48eevXVV3XPPfdo/PjxGjdunGJiYvTwww9rxIgRio+P1yuvvKJcuXIpa9aszrRrbdq0UdOmTZU1a1Y98sgj6fyNMjbvOSYkJET169dXbGysEx6Th57atWurRo0aevfdd9W1a1e98847ioiIkL+/v/r06aOffvpJ//nPf9LlO6Q3S9ZdzlufTzzxhB577DFNnz5d69evV8uWLZ2Rx7t06aLChQtrwYIF2rhxo7OdgIAAvf/++8qfP7+2b9+u2NhYbd68OT2+kmtkyZJFcXFxTn/dxMREZ9yV6tWrq0yZMlq7dq2WLFnirL+Sd6T8X375RTExMTp+/LjzcsbbfQ5/St51YPHixerWrZuio6OdMRMkOX/rqTW39v770KFDmjt3rsaPH6/XX39dHo9HFStWvAHfwB3s/w+yd6Vdu3Y53RQ7duyoNm3aaNCgQRowYIA2bNigL7/8Un5+fgoPD1ejRo00ZswYRUREaNu2bVq5cqWmTZumf/3rX6pXr56kNHiAfV3fyyPTioyMtBdffNFat25ty5YtMzOzY8eO2YsvvmhFixa18+fPm9mfTU1Pnjxp3bt3t1KlSvk02fv111/t66+/tilTpthPP/3kLKd55fWTvElUak3SJk+ebJUqVbJJkybZ7t27UzTfjo2NtZEjR5rH47HVq1f/5c9aunSp01ze7VatWuU0503NnDlzrHLlyhYWFmYhISE2atQop0lpZGSkNW7c2EJDQ51j/MqmZxcuXLBZs2ZZ48aNzePxWK9evezEiRPO+tSaquFPM2fOtAkTJti2bdt8lj/xxBPWuXNnGzlypBUuXNiaNm1q33zzjc9xd/HiRTMzmz59upUpU8aqVq1qgwcPtkuXLt3Q73CjeM+X7733ngUEBNjKlSudZb///ru99tprFhwcbMuXL3eWL1myxBo2bGgej8dq1qxpixcvdrbnbTKJtOc9Jx86dMjCwsKsdevWduzYMTPz7Yp07tw5GzhwoFWqVMmKFy9unTp1csqZ+XbryCySX88uXLhgv/32m3PcbtiwwTwejwUFBTnnBu/f/9q1a61UqVI2cOBAp7z3unfs2DH79NNPzePx2CuvvJKp/w5Onjxp9evXtxdeeMFnubfet27dahUrVrR27do5dXzlde306dPWqFEjpz7Pnj3rrMuMx+z/kpCQYBEREdazZ097/fXXbfjw4c7f+bp166xIkSI2ZswYp6xZyvvlAwcOWJMmTaxChQpWsWJFe//992/sl0hnyY+rs2fP+hxzc+fONY/HY/v27TOzP4/lkydPWteuXa1SpUo+61566SUrWrSoFS5c2IKDgy0sLMzJPGmBkI5/JDY21t58800LDAy0Fi1a2IABA2z58uXO+lWrVllISIj169fPzHwvohEREebxeOzNN9/8y5+RWpDEtfnpp59s6NChzoXzypP4wYMH7fLly3b58mVr2rSpdezY0We99+Tv/e+WLVusbt26VqNGDTNLutnZvHmzRUZGmpnddDcy27dvN4/HY2+//XaK4y0hIcFGjx5txYsXt/79+9vkyZNt8ODBVqZMGWvRooVTburUqVakSBEbNWqUmaU8bs+ePWtdu3a1ChUq+DyAunz5Mg+hkvHWRfL+i02aNLGAgAArVKiQlSxZ0j744AMzS6q7999/3woVKmSlSpWy6dOn27Fjx3zqfv369TZ69Gg7f/68xcTE2ObNm30exmSk88vBgwd9Hk48++yzVqdOnVTLVa1a1Z577jnnhiU+Pt42bNhgpUuXNo/HYxERESk+x030jeE9z44bN86KFSuW4qY6+c34kSNHbO/evSnWZWRffvmlz3e+0sSJE61mzZrWrFkz27Bhg/M33rt3b/N4PPbll1+m+Ezv3r0tPDzclixZYmYpr5HPPvusPfnkk2aW8ev4yrFmkmvUqJG9+OKLZpZ6PQwbNsxCQkLsww8/dJbFxsb6bO+TTz6xTZs2Of+Oj4/nGmip1+e5c+esU6dOVrRoUStbtqxzj2WW9ACpZcuWFh4ebocPHzYz3+vZ2rVrnYet69evt2XLlt00L02ut8TERHvrrbesXr161rVrV2f5woULrXDhwvbVV1+ZmW/9rVq1ygICAmz06NFOvcXExFhkZKQtWbLEGVcr+c+43mjujn/kq6++0pdffqkXX3xRQ4YM0cCBA1WlShVnfcmSJdWoUSN9/vnnioyM9Gmyl5iYqPz582vq1Kn68ccfU2zb/n8T6szet+56mD17tj766CN99dVXzjL7/81/+vbtq969eysuLk6nTp3SXXfdpVtvvVWXL1/WsWPH9Mknn+jdd99Vv379nNFbg4KC1Lx5cx08eFDt2rXT8OHD9dRTT6l///6SdNP1LXv00UfVqlUrzZo1S3v27PFZ9/vvv+uLL75Q06ZN1aVLF3Xo0EH9+/dXw4YNtWnTJqd/b82aNVW+fHl9/PHHOnToUIq50G+//Xb169dPq1evVsmSJZ1mmVmyZKGP7/+XvLme978//PCDsmfPrkmTJmnChAkqVqyYRowYoc2bNytLliwqUaKEHnvsMT344IOqU6eO8ubNqyxZsig2Nlbr16/XG2+8oRMnTsjMdNttt6lo0aK69957nWlXMsL5Zf/+/XrhhRf0zDPPaPz48c4otPfcc49OnDjhzMzgPR7vv/9+NWnSRGvWrFFMTIykpGaVJUqU0GuvvaZ///vfCg0NTfFzvM2Hce0uXbqkQ4cOXdNnOnbsqAcffFCLFi1y5un2jhsiJf2N3H///T5TA2Xk0ccPHTqkvXv3qmfPnpo9e7bi4uJ81u/atUtPPPGEJk+erLCwMFWoUEEFChRw/sZ79eolf39/LVmyxOma591Gly5dZGZavHixoqKi5Ofn59N0vnjx4tq9e7dOnTqVoetYUoruRYmJiU49hIaGauPGjT5T2XnLSFK7du1033336YsvvtCvv/6qLVu2aPr06T5TWj355JMKDg52zsFZs2bN1NdA73GW2nGVK1cutWrVSg8++KD8/Px07733OuXz5s2rBg0a6Ny5cxo8eLAuXryoLFmyKCYmRuvWrdPIkSO1c+dOJSYmqlSpUqpatWqG7dr1V1avXq0qVapo7ty5CgkJ0QMPPKBLly5JSppe+O6779aiRYsUHx+vLFmyON07H3roIUnSnDlz9Ouvv0pKGhOkYMGCqlGjhmrXri3pz/FC0uIY5oqLa2b/f25iSZo/f74eeeQRPfPMM8762NhY5ciRw7kprlOnjtavX69XX31V77//vnLkyKHNmzfr22+/VYcOHbR//349+OCDKX5OZj5pXy/eG7qePXvq559/1vz58xUaGuqcfE6fPq0ffvhBzz//vG655RbdcsstKl68uGbOnOn0s86TJ48zOMycOXN0/vx5derUSTVq1FBsbKxmzZqlffv2qX379jft+AE5c+ZUq1attHTpUk2bNk0DBgxQzpw5JSX1Qf/jjz/Url075c2bV2fOnNH48eM1Y8YMhYWFKSAgQImJibr33nvVoEEDbdq0SaNHj9Zbb72VIgB6++ZfvnxZWbNmzRAB8XrwnlOyZs2q2NhYTZo0SWam//znPzpw4ICaNWvmhMY777xTx44d07BhwzR16lQVLVpULVu21JAhQ9SrVy+1atXKGfhw9uzZypo1qxo3bqzbbrvN52fe7Dfa3jqbN2+ehgwZov/85z+qVauWHnvsMeXOnVtS0sOnuLg4zZ07V+3atXPGj8iaNavuv/9+Xb58WTt27ND999/vnG9r167t3Hzg+pg0aZIWLFigO+64Qz179lSJEiX+sry/v78SEhKUPXt2derUSa+//rrmzJmjwoUL/+Vxm1HPJ4cOHVLv3r3l7++vt99+W88++6yKFSuW4mHwrFmzdOHCBUVERKhIkSK6++67nXWXL19Wnjx51KlTJ73//vsqV66cmjdvruzZsyshIUH58uVT/fr1NWHCBFWuXFkNGjSQn5+fU6eHDh3SXXfdlWmmgx0/frxOnDihQYMG+Rxzd9xxhyRp+/btKlasmLPc399fiYmJyps3r5o3b6533nlHAwYM0JEjRxQVFaVp06al+Bk3+zn4evEeZ0eOHNHUqVOVLVs23Xfffapdu7Zy586tChUq6IcfftCUKVO0Zs0aNWzY0HnA36hRI504cULvvfeeGjVqJI/H4wxods8996hKlSqZpp69L/aSZ4fo6Gi99957evTRR/Xcc8/p8ccf1+233+6sDwoKUunSpbV27VotWLBATZs2dcZZkKT8+fPr8OHDmjJligIDA1N9UJ2mD6+v+7t5ZFgHDx600aNH26JFiywmJsbMzAYMGGDVq1e3s2fP2m+//WYTJkyw119/3dq3b29vvPGG05Ry+fLlFhISYqVKlbJmzZpZqVKlrGvXrnb69Ol0/EaZg7f5zuTJky04ONjeeecdZ926dessJCTEZ7qeixcv2saNGy0iIsK++OIL++677yw+Pt4uXLhgnTt3tmrVqvk0e72yie3NJCEhwamf+Ph4mzhxogUEBNj333/vlPnoo48sLCzMDh48aFOnTrWQkBALDw+3GTNm2PHjx32aR126dMkGDhxoHo/HVq1adcO/z80oeROxPXv2WHh4uJUqVcoCAwOtSJEiVrBgQZ/uAXFxcTZr1izzeDw2Y8YMM0v6Pc6aNcuqVKliHo/HSpUqZcWKFbOXX37ZoqOjb/h3ulHOnj1r9evXt/bt29vOnTtTNEc/efKkVatWzRo2bGi//fabz7qpU6eax+Ox7du3p7rtjNQNIL0cPHjQ2rRpYyVKlLAhQ4bY2LFj7eTJk9e8nRYtWlhwcLBt2bIlDfbS3Q4cOGBNmza1Bg0a2IoVK5zxJczMuS6ZJTX9rVChgg0fPtxnfUxMjF26dMmnKXFYWJg1a9bMfv31VzP7s/95bGyszZs3z+fnx8bG2tKlS618+fLWoUOHm64r15VWrFjhcy5IrbtVdHS0vfrqq+bxeKx///5OPZmZbdy40eecfOVno6KibObMmebxeMzj8Vi3bt18xkpA6iZNmmSBgYFWrVo1q1q1qnk8HmvSpImtW7fOzJL6+zdo0MAqV67sfMZ7vr906ZItWbLEWrdubfXq1bP69evb22+/nS7fI70kv/bFxcU5f9OrV682j8fjM72f92/Y+99ff/3VatSoYWXLlrUff/zRTp8+bXv27LGePXvayy+/7Nxv7Nq1y8xubHcXQjr+p/j4eBsyZIgVLVrUqlatas8//7xz0l2+fLlVqVLFgoODrVChQlalShWrVauWNWvWzAoVKuQzEMtPP/1kffr0sRYtWly1jx2unyv7lSUmJlrjxo2tVq1azgX2jTfesOrVq/+tG48zZ85Y8+bNrVWrVnb+/PkMdRN/7Ngxi4uLs5MnT1qDBg2sbdu2zs10ZGSkFS5c2EJDQ61YsWI2cuRI27dvn89FYcuWLXbw4EEzS+oH1rlzZ9u6dWu6fJeb0fr1623q1KkWERFhXbp0sW3bttkPP/xgr732mnk8Hps9e7bPOeLIkSPWqVMnCw8P95nf+NixY7ZhwwZbtWpVihvRjOjzzz83j8fj9Kcz+/Om2VtfM2bMsBIlSlirVq3s4MGDduLECVu/fr3Vq1fPOnXq5DxwxfX3/vvvW5kyZWzu3Ln/p4dF3uM2MjLS58FhZrJ+/XorXLiwffHFFz7Ld+zYYWXLlvXpF9q4cWNr1aqV/fDDDzZ37lzr1auXPfnkk1amTBkbOnSo7dixw8zM5s2bZx6Px8aPH59ivBWv5NfOIUOGWM+ePX0GnLoZecdeGTJkiDOYr5f3Gpj8XDl9+nQrXbq0NWzY0KKioszM7MSJE1amTBknBCavp6NHj1q/fv3M4/FYo0aNfPqdZ9Rz8PWwY8cOq1y5sg0ePNh5YbJhwwarU6eOVaxY0Sn34YcfWpEiRWzs2LFmlvoYIWfOnMnU5/QpU6ZY3759bfr06WZmtnfvXgsMDLRp06ZZVFSUzZ492wYPHmwdOnSwxo0bO2NorVy50urXr28ej8cqV65s1apVs5IlS9qyZctsy5YtFhQU5Iw3dCMR0vGXLl68aD179rQqVarYxIkT7ZdffvE5uScmJtqBAwfsgw8+sJUrV9rGjRudk/GwYcOsTJkyPk++zcxntHBO3Gkj+ck7NjbW+Z0tWbLEgoODrX///hYbG2uNGjWyIUOGmNnVfxexsbEWGRlp48aNs0qVKjlvLzOK2bNnW2hoqDMQ0aJFi6xgwYI2e/ZsMzM7deqUvfTSS1a4cGFbunSpz/F8+fJl++KLL6xu3bopbiLx97Vt29Y8Ho/VqlXLZ+DJgwcPWu3ata1169bOQxCvlStXWrFixWzUqFFXHbAl+WjYN7uzZ8/aG2+8YWvWrHGWzZ492woXLmwHDhwwM9+/++Tfe8KECVaqVCkLCAiwGjVqWGhoqFWsWNF++OGHG/cFMpno6GirVKmStW/f3md5Rjkeb5TVq1db6dKlbeLEiWZm9v3339uWLVvs3LlzVrFiRevYsaNzbti0aZNVqFDBPB6PBQcH25NPPmldu3a1Pn36WKFChaxPnz7OduvUqWPBwcE+A3Fdyfu7utnfnntdvHjRXn/9dStTpozzoP7MmTPWrVs3CwsLsxo1aljv3r2dQcjMzBYvXmxVq1Z1WjJcvnzZateubX369EkxO0ZUVJS9+eabPvcIiYmJ3OfZXw/G9/rrr1u5cuXszJkzzrIVK1ZYqVKlLDg42H7++WczS3oI8swzz1iJEiWcF2Xeur1ywNXMZt26dValShULCwuz559/3iZMmGDx8fF25swZGzZsmBUpUsQZYLZhw4ZOq4OQkBDnAeiZM2dszJgx1qtXL+vTp4/zoP/8+fMWEBBgY8aMueH1S590/KUDBw7o+++/V+vWrdWmTRunn66Xn5+fHn74YbVv395n+alTp3TgwAHdcccdunDhgs/nsmXLpsTERJ/+Xri+vH1kJk+erMjISBUpUkRPP/20atSooS+++EJr167VBx98IDNTQECAJN/+jJcvX1ZiYqI+/PBD7dq1S3v27NH+/fvVqVMntWjRIl2+0z+VfMCl5PLkyeMzGFBYWJgqV66siRMnqmzZsnrggQdUr149rV69WsuWLdNjjz3m9FPavHmzpk2bprx586po0aI+2/X2GUOS1OojPj5e2bJl0/Dhw1W1alX99ttvKlCggFM+X758eu6559SrVy+tXLlSLVq0cPqhFitWTPXr19fEiRNVq1YtFSpUyGfbZpZh+uLt2bNHkZGRWrhwoY4ePapy5co5508/Pz+tWrVKTz31lFO/V373Tp06qVy5clq6dKlOnjyZYhwR/HMxMTE+gzL5+/s710cpaYCy7NmzZ5hjMq3Z/x9zoWTJkipSpIhmzJihVatWaePGjerdu7eCgoL06quv6sUXX9TatWvVsGFDBQcHa+rUqfrjjz+ccRYeeOABSdLhw4d1+PBhnTp1Srlz59Zbb72lPXv2qGDBglfdB+/v6mYbCPVqcubMqZYtW2rZsmX69NNPdd9992nUqFHau3evqlWrpqNHj2rp0qU6cOCAunXrptDQUNWqVUuPP/64evbsqX79+mn8+PF6+OGHdeTIEeXIkcPnunrPPfeob9++zs9j7JUkya99lmxMJ++AfL///rtCQkJ05513as+ePRo8eLB+/PFHNWzYUK1atdJjjz0mSbrvvvvUsGFD7dixQ0OHDtWYMWN8BpFM/t/M5MSJExozZozuvvtu9ejRQx6PxxmX5c4779Qrr7yi4sWLS0qqw3/961964IEHdObMGZUrV067d+9W2bJldeedd6pbt27OcStJZ8+e1axZs3T58mUVLFjwhtcvIR1/6dSpUzp37pxKly6tnDlzKjo6Whs2bFBcXJxiYmJUsWJF3XvvvU75CxcuaN++fVq9erW2b9+uNm3aOH8syXGjkrbWrVun/v37Ky4uTsWKFVNCQoJzE/n888+rQ4cO+uijjxQdHa3hw4dr+fLlqlChgsqWLauHHnrIOUF5b2qKFy+uCRMmODc8N5u/Cmw5c+ZU7ty5tXr1auXPn1933nmnOnTooA4dOmjmzJnq3r27KlWqpF69eun111/X8uXL9e9//1tZsmTR9u3bVbp0af33v/91BuPzyuw3Jl52xSwNv/zyi7Jnz64HH3zQCTV58+bVc889p3feeUdr1qzRo48+6pSvWrWqwsLCNH36dJUsWVKFCxeWlHTxrVevnrJmzZqi7qWMcbOyZMkSvfnmm7rzzjt1/vx5SUl/21988YXq16+vEiVKKG/evFq6dKlq1KihvHnzOg8+JOnHH39U9uzZFRQUpMKFC6tw4cI+N9XJb0bwf/PDDz84A6LWq1dPFSpU0B133KFz587pnnvu0aZNmxQfH6/s2bM7N+jJb9T/SvJy3nB5tYeNGUnyUHPrrbfq7NmzOnHihC5duqSxY8c6N9zVq1dXWFiYpk2bpqJFi6pgwYLKnz+/8ufP72zr8uXLOnDggKKiolS0aFHnfuSxxx5zwk9G5x153d/fX/nz51ebNm30zjvvyOPxaNeuXerVq5cqVaokf39/rVixQr169dJ7772nwMBA5cqVS//+9781bNgwTZo0SW3bttVDDz2ko0ePOsfklSzZQKD489o3ZcoU/f777ypdurSqVasmf39/+fv7K2vWrNq/f79ee+01ff755ypatKgmTJig4sWLO9fIc+fO6fbbb1elSpX09ddfa8mSJTp8+HCq177MZu3atdq8ebMmTJjgMytJ8uO+WrVqPp9JSEjQhg0bJCnFoLJZs2ZVTEyMdu3apV9++UUffPCBypQp45x3bqgb+t4errZw4UJbvHixbdu2zWd59erVrVy5ctasWTMrV66cVa5c2QICAszj8VjTpk2dwWymTZtmrVq1sjp16lhgYKDTbwY31smTJ61Vq1bWuHFjW7t2rZ06dcpZ522qM2zYMCtVqpR169bNZs6caQ0bNrSCBQtaQECANWvWzEaMGGHffvuts72byfnz523//v1m5tu14vDhwzZ8+HCnr7i3mdjJkyetVKlSNm7cOKdsbGysvfnmm1aiRAmfv4f169fbm2++aT169LBu3br5NM3OrM3M/q4tW7ZYkyZNrFixYhYYGGgtW7ZMMV9x2bJlrXnz5rZnzx4z+7NON2zYYEFBQTZy5EiffqEZsc6932nLli0WFhZmzzzzjH3//fe2bt06+/DDD83j8diTTz7p9BF96623LDg42AYMGOBs4+LFi/bjjz9a3bp1bcKECan+jIxYdzeKt+6++uorK1u2rDVo0MDCw8PN4/HY66+/7nQ7GDRokIWEhNjMmTPNzLfZ9OnTp23JkiWp9lVP/rvZtGmTtWrVynr37p2WX8mVtmzZYjt37rTSpUtbrVq1rHTp0k53D+/5+5dffrHChQvbW2+9laIv7m+//WaLFi2yzp07W/ny5e2777674d/BTbzd3o4dO2bNmzc3j8djHTt2dNZ763TkyJFWokQJn7nOvfr162ehoaEWGBiYacdJ+F+u7NJy/Phxa968uRUrVsyKFStmHo/HBg8e7JRbsmSJBQQEWMmSJW3GjBl28uRJny4CP/zwg/Xq1cuOHDliZma7du3K8IPxXUu3oMGDB1uxYsWcOvlfn923b599+eWX1qJFC3viiSdS1OW5c+esffv2VqtWLad76JXddm8UQjrsyy+/tLCwMKfPYnBwsH344YfOgb53714bMmSIdezY0caNG2dz5syxP/74w5YuXWpBQUE2adIkM0sa1KJbt242dOhQn4OePnhp42r1umjRIvN4PD4B0izpxs/7mdOnT1vVqlWtadOmzu/q119/tXnz5lm3bt2sTJkyKUa5davLly87o8vHxsba4sWLrXnz5nbo0CFn2U8//WRz5861wMBACw8Pt8jISJ/6a9OmTYq+o/v27bNKlSpZjx49/ufo9fS5u7rLly/bJ598YuXKlbMOHTrYRx99ZJ9++qk1btzYSpcu7fMQZP78+ebxeGzcuHEpBj0cMGCAeTwe+/HHH1P8jIxyjomOjnb613oD+ZUjeo8aNcpKlSrlPAS9cOGC9erVyzwej7Vv396GDBlir7/+uoWHh1vdunWd/oy4fryzkgwYMMCGDRvmnGteeuklCw0NtU8++cTMks4h3kGIdu/e7Xz+999/tyFDhlizZs18rpXJj/njx49b7969rWDBgta2bVvbtGlTpnqwMnz4cCtVqpQdPHjQzp8/bytXrrTw8HDr0KGDU8b7dz9o0CArXbq0c244e/asvfDCC85MEVcOYpbZJCQk2NChQ+3FF180s6SxK5YuXWoBAQHWtGlT50G89+HSsWPHrGLFivbMM884/aS917izZ8/arFmzrGDBgrZ06VJn+0iS/F7AWy9Lly61Jk2a2E8//WTbtm2zoUOHmsfjsblz55qZ2aFDh6xDhw7OvYnXhQsXbN26ddaiRQvr1q3bTffC5Fpt2LDBhgwZ8rf713vr9/3337eiRYs6Y7Mkl3xbO3bssA4dOljDhg2taNGi9tRTTzkvdK4sv2bNGps6dWqKWVFuNEJ6JhYbG2sTJkywChUqWL9+/WzFihW2Zs0aGzp0qBUsWNAWLVrkPPn3vpFM/kcTFRVlBQsWtPfee8/Mkt7eJA8zqU3tgX/ufwXCkSNHWmBgoHOzf+UF1Pv5Tz/91IKDgy0iIuKaf4Zb/Prrr1aqVCmbNm2as+zbb7+1EiVKWJ8+fezTTz81j8djderUsXPnztnWrVutbt26Vr16dWdwm9jYWBswYIDVrFkzxcOlTz75xAICAuzrr79O9edzc+Irtfo4evSoVa9e3QYPHmy7d+92jq3Vq1db0aJF7cknn/Qp37RpU6tcuXKKKX4OHDhg7777boat8927d1tAQIB9/vnnzkOJkJAQO3HihJn9+Rb29OnT1rRpU6tVq5YzEvDvv/9u06ZNs/DwcKtQoYJVqVLFBg8e7NOSBP/cnDlzrEGDBta6dWubNm2aNWrUyGcWh3379lnDhg2tdu3a9vvvv5uZ2cyZM61ixYpWrlw569+/v40YMcLatm1rJUqUsLFjx6YYlCw+Pt4mTJhgwcHBVr16dZs3b16GGa359OnT9v333/sMdnXltcb79z5p0iQrWbKkTzB5/fXXfVomeD974sQJK126tPXq1cspv3jxYnvjjTd8zt2ZoQVJatfuU6dOWdWqVe21115zlp05c8a6d+9uoaGhtmHDBme593js06ePVa1a1aKjo1PU2fHjx61evXo+rXcyu+TXpXPnztnQoUOtf//+9sknn9jkyZPt448/dtYfO3bMnnnmGatWrZrTauyHH36wkJAQq1Wrlk2fPt2+/PJLe++996xatWpWt25dZyq2jKxv375WrFgx5wXR373Wr1692gIDA53WkMlfSJklnR9OnDhhhw4dsmHDhtmrr77qM0Vu8rJuOz8Q0jOhM2fO2M8//2wxMTH2zDPP2IABA3xGTt6xY4cFBQXZ008/7TN/tld8fLz9+uuvNnjwYKtSpUqqc7dm1Bvp9Jb8BPL999/b2LFjbe7cuc7I5GZJ3Q4CAgJ83tyk9nkzs1atWlloaOhN3WytQoUK1qJFC2cOSzOz7t27W8GCBS0oKMhGjBhhmzdvdm5eDh48aO3bt7ciRYrYrFmzzCxpqpmSJUumeBJ74sQJq1Gjhg0cOPCmeXCRHq78e7/yrdWYMWN8Rgz+5JNPLCQkxMqUKWMej8en1cbWrVudZsP/qwXDzS75zcTPP//s8+Z8woQJVrRoUZ/zq/cYnDZtmnk8Hhs0aJDP9i5evGhHjx71CTYct/9cYmKizZw508qVK2ft2rWzOnXqmMfjsTJlytjRo0ctMTHReQs5depUK1WqlDNrRnx8vP3000/Wrl07CwsLs8qVK1vz5s2dh1DJffXVV87UP2PHjnUe0GQE8fHxztzb3mBypeQP9r3zca9evdpZv2fPHqtbt641bNjQOca9x/fHH39sAQEBNn/+/FRvtDPb30Hyhz+xsbEWFhZmb731lk+ZjRs3WrFixWzAgAFO9xmvAQMGWNGiRa/69rZOnTrWp08fHgRe4ejRo1atWjUrX768hYaGOvPFL1682Mz+vFb++OOPFhQUZMOGDbMLFy6YWdLb29atW5vH47FSpUpZmTJlrH///s76jMpbJ7/99ps1adLEnnzySee4+ztZ4tixY9a1a1crVqyYbd++3Wca0l9++cVeeOEF+/TTT83MUtSl288LhPRM5uzZs/bUU09Z/fr1zSypL0zyk/mQIUMsICDAateubQULFrSxY8c6/ZguXrxoU6ZMsTFjxlijRo2sWLFiNmnSJNc9ecrovE9hixQpYuHh4c60Ep999pklJCTYxo0bLTg42IYNG2ZmKZ8qHj9+3Am0S5cutYYNG/7lNDRu5b0p/v777505b2NjY23Lli1WpUoVCwwMtLp16/pMa+KthyNHjtigQYMsMDDQBg8e7ARD79Pq5CfujN73659KXlfx8fHO3N0LFixIUfbgwYPWunVrZ87RtWvXWtOmTS0sLMynXNeuXc3j8aT68CijnG9+//13n3PvvHnzrGjRok6A2bp1qxUvXtyGDx/u3Ah7/7tlyxYrXry4VatWzXkjcGVrp4w0/Vx6iY+PtwEDBlifPn2sd+/eNnLkSOfm0dv94vPPPzezP0PR+fPnrX379lahQgWfh1WxsbF24cIFn+aTyc/NR48etdDQUHv55Zdt165dGfJ3t3XrVuvVq5dPIPz++++tXbt2dvz4cZ+yW7ZssbJly6YYU2HixIkWEhJio0ePNrM/j/fY2FgLDw+3t956K9Umx5nFuXPnrHPnztahQwenOfqFCxcsICDAaT2WvM6GDRtmAQEBToD3XkPDw8Ota9euKbYfFxdnO3fudFrrZFZXHlfr16+3QYMG2ZgxY+y5556zyMhI27Nnj02ZMsU8Ho+NGjXK53x/6dIle/31161EiRK2fv16n+V79+61LVu2OK1xMgPvMTl58mQrX768zxhBf8fatWutevXqVrFiRfvwww9t48aN9vnnn9vTTz9t1apV86ljs5vnvEBIz4Q6d+5szZs3d/rVmSUNvlK7dm0LCwuzTz75xE6ePGndunWz8PBw54n//v37rXr16la1alXr2rVrir4cuP6ufMoXGxtrr732mtWpU8cWLFhgu3btshMnTlinTp0sLCzM5s+fb5cvX7aXX37ZAgIC7KeffnJORpcvX7ZffvnFunbtah988EF6fJ3rzntif+aZZ6xixYq2detWi4uLs02bNtn777/v85Y2ISEhRcAbMWKElStXzmrXrm3lypX7y8EO3f7ENT3FxMTYwIED7Y033rC2bds6bwu9x15iYqLFxcXZkCFDrHLlyrZy5UrnifbLL79sHo/H3n33XWd7Z86c8ZlrN6Px9r2vW7euTZ8+3RISEmzu3LkWHBzshPQzZ87YgAEDLDg42L799lufmwpvv/Q6depY586dU+2OhH/u3LlzNnDgQPN4PBYeHu7zwG7nzp3WokULCw0NdZZ5fw9Lly61SpUq+YScK88fqQXJAwcOZJg5uf8X79//xx9/7PQbv3IclSpVqtjrr79uZn8+BDlz5oy1adPGKleu7LT08z6wTX5Pkxmkdk06ceKETZw40Ro1amQej8eaNWtmkyZNssDAQOda6K0vs6S3l3Xr1jWPx2Nt27a1Dh06WJ06dSwkJMRWrFiRYvvR0dEWHh5uxYsXdwaXzcgOHjzotFRMLdh5/+b79+9vAQEBVqlSJZszZ46zPjEx0Vq3bm1VqlTx6VZgltQypGLFitajRw/7448/0vBbuNeVXVDOnj1rbdu2tbp16zp/338VqK/sc16/fn3zeDwWFBRkgYGB9sQTT9j27dvT9kukoYw9jwd0+fJl5//Hx8dLksqWLatff/3VZ3qMefPmSZLefvttNW3aVLlz59b999+vI0eO+Myt+/7772vy5Ml655139MgjjyghIcGZYgnXn3fqjh9//FGSdOjQIS1cuFB16tRR/fr19fjjjytPnjy6//779ccff2jbtm3KkiWLWrZsqccee0xdunTRu+++qx9++EFz5szRqFGjtH37dmdO6Zvxd5d8n71TbAwcOFDHjh3T7NmzdeHCBQUHB6tOnToKCwvTmDFjdPr0aWfe4uSfe+GFFzR48GAdPnxYUVFRiomJ8VmfHFOqJbnymNm5c6fq1aunb7/9VlmyZNHdd9+t7Nmza/PmzZo6daqkpOnQzpw5o/nz56tSpUqqVKmSbrnlFmcb+fLl09tvv60jR45ISpperUWLFqn+vIygYsWKmjJliu666y4NHjxYTz/9tCZPnqwHHnhADzzwgBISEnTnnXeqVatWeuihh9SnTx9NmzZNO3bs0MKFC7V+/Xq98cYbqly5siIjIzVnzhxJGbOu0lOuXLnUuHFjBQcH6/z58zpz5oxzbvB4PGrcuLFOnz6t0aNH+3yuWrVqCgwM1LJly/Tzzz9LSnn+SP5v75RqDz/8cIaZk/uvjBs3Ts8++6wuXbqk1q1ba8aMGTp//rxee+01LVu2zCkXFBSk9evXS0qaq9z7d9GiRQvFxMRo1KhRkuTcy9x1112SUj9/ZySJiYkyM+cYOnTokGJjYyVJefLkUceOHTVnzhwNHTpUuXLl0ttvvy0/Pz/99ttvunTpks+9X758+dS2bVtJSfUXHBysatWq6euvv1Z4eLjPzzUz3XHHHerYsaOWLl2qcuXK3aBvnD6OHDmip59+Ws8//7ykP/9OzUyJiYkaPHiwxo0bJ0l6+eWX9fDDD+vo0aPOVLWxsbHy8/PTK6+8osOHD2vx4sU6d+6cs/38+fOrbdu2WrRokTMdWGZy+fJl+fn5yc/PT/Hx8YqLi9Ptt9+upk2bKiYmRp9++qmkv56y2XtPZ2YqVKiQPvzwQ02fPl0RERGaMGGCZs2apcKFC8uSXkrfkO91XaXPswGktYSEBBs9erR17tzZeQrofeK0YsUKCwoKcp5aHzt2zIKDg61v374+2+jZs6dVqFDBPB6PffPNNyl+Bm8W00byet2zZ481btzYatSoYYcOHbIVK1ZYSEiI88Zg4cKFVr58eStVqpR9+OGHPmML7N+/35o3b24FCxa0wMBACwoKsubNm98UTdtTeyOY2iBDZn/W15tvvmlFixa1JUuWOOsWL15sQUFBzuB4Vz6R9f6cVatWWYMGDZxuIPjT8ePHbcOGDT7dBpIbO3asBQUF2bJly5y3Xbt377a6detaxYoVnWljDh48aDVr1nTeLkZHR9vixYutevXqNmnSJOvbt6/TvzeziImJse+++86ef/55CwwMdFp+HD161Cmze/dua9iwofN2wOPxWJs2bezSpUt2+PBha9KkibVo0eJvTz+D1O3cudP++9//WkREhM2ZM8c5bs+fP28ff/yxz2jM3mM0KirKXnzxRStYsKBT/8n/BjLziOJmqd8jJCYm2meffWYej8cWLVrk04Wje/fuFhgYaJMnT7aEhASLiIiwqlWrppiS0SypS8yoUaNSbSGVUWzZsuWq512zpMHGmjRpYhUrVrTy5cvbsGHDUoxFk5CQYMOGDTOPx2NhYWHWrFkznze9Zkn3gDVq1LAhQ4b49DG/8veX2c4t8fHxNnHiRCtYsKDNnz/fzP6sg927d5vH4/GZncTb1Sv5AH3eOnz11VetePHitmzZMp+fcezYMXv33Xcz/PgryV359zp58mT773//61M3PXr0sCpVqjjduf7JsXczZxVCegaVkJBgM2fOtICAAGvSpInPCLS//PKLFS1a1GbMmOH8sTRu3Ng6duxov//+ux0/ftw+++wzq1+/vu3YscMmT55sly5dSq+vkikdPXrUdu7caZ999pk1b97cli1bZufOnbOdO3eax+Ox6dOnW+vWrS0gIMAGDBhgu3fv9mkm6R1H4NSpU7Zt2zZbtWpVqgMVudGZM2echw3eZnnJT9DR0dG2aNEi++mnn3weSiQmJlqpUqWsU6dOzvKoqCh79dVXLTAw0GewIu/cxN7tJiQk2Ntvv23h4eE+g/AhaUq/6tWr21dffWVmSc0jJ06c6ISYzp07W61atZzy3t/Z3LlzrWzZsta/f38zSwov3pkjnnzySXvxxRctLCzMXn311QwzevW18h5/Fy9etJdeesk8Ho8VL17cKlSoYDNmzHCC36lTp2zNmjU2bdo0n4G0zJIG4StdunSm7h/6T8THx9vo0aOtcOHCVq1aNQsLCzOPx2M1atSwX3/91cz+HEuhZs2aKZqlrlixwsqWLWvPPvusmaV+M5lRQ+TVXPlA9cr7hzNnztgzzzxjdevW9el3m5iYaD179rRSpUrZ22+/bV988YUFBwf7vGjwbjej35P8+uuvVrBgQevXr5/Pcu+xNGPGDCtWrJh17tzZxowZY4MGDbJixYpZ27ZtnXOz955g4cKFVqhQIfvss8+scePGTjP4jz76yBkL4MqxVzJbIL+St55/++03a926tZUvX97nHmv8+PFWoUIFi42N9VneqlUrq1atmv3www9m9uf18OTJk1ayZEnr2rWrzyCqmdmiRYssLCzMypcvb+3bt7e5c+c6dblx40arUaNGpu/ORUjP4JYuXWqNGjWy8uXL2+rVq50LXPK+XgkJCfb111+bx+OxihUrWrNmzSw4ONheffVVu3jxorOtzPgHkh5OnTplzZs3N4/HY/Xq1bNFixY56/bs2WNNmzY1j8djzzzzjP3www8pnsBOnjzZZ3qJm8n58+etUaNGVrFixVTXT5w40YoXL26lS5e2QoUKWcWKFW3RokVOHcyYMcM8Ho8zkqdZ0tuGihUrWqdOneznn3+2pUuXWuvWrZ0WBd6bkYULF1rhwoV93mIiqe9oSEiItWvXznr27Gkej8c6d+7s3Fy3aNHC6tat69Sb9xxz/vx5e+aZZyw4ONh5o3j48GF77733nCnEkvdDN8u8N4axsbHWvHlze/nll23ZsmX27LPPWnBwsFWrVs2GDRuW6gjK3puZCxcuWOfOnZ2ZCnBtfv75ZwsJCbFhw4Y5D/Jmzpxp5cuXt0aNGjkDxS1ZssSKFCmSYkCjixcvWr9+/czj8TBOi/neJ+zbt8969OhhL730ks2YMcN5eJyYmGg//PCDFSlSxN577z2fPtLR0dFOywXvtc7bl/rKe5ArB0XNSM6dO2cRERFWtGjRFK3fzpw5Y40aNbKePXv6HHPecViunBpt9OjRVq5cOTt37pxduHDB1qxZY23btrWCBQumuFe4md86Xk/Jj6t58+ZZQECAM1hhXFyctWnTxrp162Zmvg+Pvv32WytevLi9/PLLznnbe3xPmDDBPB6P81Y+M1u1apWFh4dbnz597Mcff0wxcKSZ2fDhw50H1maZ8/6AkJ4JHDx40Jo2bWoVK1a0SZMmmZnZiy++aI0aNfK5+Zs3b57169fPOnbs6NNk2IyAfiN8/fXXztPszz77zMLDw61MmTI+zdcSExNt1KhRVrBgQZ95N82S3hp/+OGHVqZMmVQHfLlZzJo1ywoVKuQ0yfMeox9//LGFhITYG2+8YatWrbI5c+ZY8+bNrWjRoj51UbduXWvQoIHTDO38+fM2c+ZM83g8Vrp0aQsICLD69ev7XBTOnz9vY8eOtVKlSl11eqDMyHtRfO655ywgIMBCQkJsypQpPiOTv/feexYUFGTfffed8znvuqlTpzoPlJI7ffq0zwPAzHxj6P3uDRo0sB49ephZ0jG/ceNGa9++vfXo0cMuXrzoMwjflZ9lGqT/u1dffdWCg4Pt0KFDzrLY2FhbsGCBBQYGOoNJRkVF2SuvvGIhISEpml/v3bvXp1UPkqaYKlGihFWtWtXCw8PN4/FYnz59nGtcTEyMvfbaa1amTBmf6TO95syZYzVr1jSPx2OjR4/OlDfokZGRVrNmTevYsaPP8g0bNlhwcLCtXbvWzJKOwylTplixYsWsSZMmtmjRIouPj3fq7J133rGwsDCfhyEXLlxwWpThT8mvRd6/72PHjlmvXr0sICDADh48aLGxsVaiRIlUZzAxS+oqGhoaagsXLjQz33D5ySefpOHeu0/yYy65wYMHW6VKlXzOu8kHgTP7M7s0b978f3bnSn5dvNq0gTcjBo7LBPLly6exY8eqXLlyioiI0KeffqrY2FhlyZJFUVFRzmAKDRs21JAhQzRx4kTVqFFDkpSQkCDpz8EZ8M/Z/x90JLmDBw/qxRdf1Pz58yVJ5cqVU0hIiM6dO6fz589LSvpd+Pn5qUGDBipXrpyGDRumSZMmacuWLVqyZIneeecdTZo0SdWqVVNwcPAN/lbXT+3atVWuXDkNHz5ckpQtWzbFxsZq8eLFeuihh9SlSxdVrFhRjRs31gcffKAHH3xQH3/8sTO43sCBA7Vz504tX75cFy9e1K233qonnnhCEydOVOvWrTVp0iQtWLBA9957r6Skel27dq3GjRunsmXLKm/evOn23dNb8mPTzOTv76+oqCj9+uuvypkzp+666y4FBgbqgQcecAa4qlWrlnLlyqW5c+fq2LFjkuSsS0xM1AMPPKBt27Y5x7aZ6a677lLOnDmdgScz86B8WbJkUUJCgqKiopQvXz5JSQPlFC9eXBMmTNCoUaOUM2dOZ/Cc5Odib71ly5btxu/4TejYsWN66623tGfPHmdZYmKi7rzzTj344IOSkgYzyp49u8qWLavw8HB99tlnio+P1z333KPGjRsrZ86cevfdd53zsSQ9+uijypcvn3O9zIy89xHz58/XuHHj9NVXX6lOnTqaMGGCVqxYoS5dumjNmjWaPn26JOm2225T69atlSVLFk2dOlWXLl3y2U7jxo31+uuvq0CBAjpw4ID8/f0zXf3++9//VuvWrbVmzRotX77cWR4dHa34+HgVKFBAy5cvV82aNTV27Fi1bdtWb731lqpWraqsWbM654zff//dGVTPO5jwLbfcojvuuCPT1elfSX4tWrNmjaZNm6a4uDjlzZtX9erV0x133KG3335bW7dulSSVL1/e5/PewZm7dOmibNmyaf78+Tp+/LjPsfvkk0/ewG+Ufrz3Ed5BCjds2KBdu3Y5g/NGR0fr3//+t3LkyCFJ+uabbzRr1iyNGTNGCxYsUHR0tPLly6c6deroyJEj+uyzzySlHETOe77w8/PT2bNnNXjwYLVt21YHDx68Id8zzaXX0wHcON4nTJcuXbIRI0ZYtWrVrHz58lakSBGnb8yVb8oz85utGyU2Ntap9xMnTliHDh3s6aefdp48rlixwkJDQ52BtpI/QTxy5Ih17drVihcvboULF7bSpUtbWFhYhmnyunbtWgsKCnLmej98+LCFhITYf//7XzP7c0ovb1mPx2MRERFOP8WuXbtaxYoVU51n2yv5E94ff/zRpkyZkkbf5uaQvD6unAbq1KlTtmLFCvN4PDZy5EifPuTx8fE2efJkZ533OI2MjLRWrVrZoEGDrFmzZta2bdtMM73UtYqOjrawsDAbM2aMz3Lv+YHz8T8THx9vx48ftw8//NACAgLs7bffdtYNGjTISpYs6UwndeVbr+QDp547d87eeOMNCwoKylRzGKfmaq3rqlevbuXLl7fw8HDbsmWLszw2NtY6d+5s1atXd8ZHiY2NtUmTJllAQIDPudr7O4iNjbXhw4dbeHh4hu+DbpZ6nR46dMjatm1rtWvXdqat814Pw8PDLSAgwHr06GG//PKLz/n1yJEjTiu85s2bW7NmzW7Ml3C5HTt22CeffGJnz55NdX1kZKQ1bdrUQkNDrVSpUjZ16lQzS2p5MHLkSGe8ioCAAOvXr58tX7481W0NHz7cp6tGZrVs2TKrXLmylSpVyhkj6/vvv7fvvvvOwsLCrEKFClamTBmrUKGChYaGWnBwsBUqVMi5771w4YJ16tTJypUrZz///PNVf87HH39spUuXtgoVKthnn32WYc4XhPRMaMqUKVa7dm3zeDz2+eefp/fuZEpTpkyxunXr+tyYvP7661a7dm1nTsdz5845o7J6b2qS36zHxsba9u3bbfXq1bZs2bIMdSMfHx9vAwYMMI/H4zxIqlmzpj3//PNmljK0tGzZ0lq2bOncxERFRZnH47EePXo4/SC9MmOzyeS8g2FdrRna2LFj7dlnn7XevXvbZ5995lOua9euVqZMmRSDl5kldaHxeDxWtWpV69y5s9WtW9dq165tZ8+etYEDB1qFChXsl19+SZsvdZPbvXu3FSlSJMU80fjnZs2aZWFhYda0aVOrWbOmBQcHW4MGDZxz6k8//WQej8cGDhzonCu8Yef77783j8fjDAJlltT0NTONxJya1M4d3vPqt99+a4GBgVaiRIkU9blmzRqrUqWKde/e3fnc0aNHrWHDhta2bVs7depUiu2OHDnSwsPDM3R3gv917V66dKkFBAQ43RVPnDhhffv2NY/HY3PmzElxjVu7dq01bNjQZs+ebWZJs5zQJSaJd1yV1OZ437lzp9WoUcNatGhhX3/9dYpuGJs3b7Y2bdpYoUKFbNCgQVajRg1nLKfevXvbvHnzbP/+/ZaYmGgXL160pUuX3qiv5UpffPGFhYaGWq9evWzevHk2a9Ysa926tTNOzXfffWeDBg2ysWPH2sKFC52HSgMGDLCSJUs69f/ll19au3btnPXJH2Z9//33Vq9ePStatKgNGzbMfv/99wzVPZeQnokk79O4adMmK1iwoHPSR9q5MhQmJCRYkyZNnAG4vvjiCzNLGs3S4/HYypUrnbJbtmyx2rVrW5MmTW7kLrvCnj17rEKFCvbcc8+ZmVn//v19BmeKj493bm68fRu9U9OZJfVp9AZSJD306dKliwUGBqb6Rnvr1q1Ws2ZNK1OmjLVu3dqZfvHFF190+o0dPnzYihQpYj169HD6iHkviOfPn7d58+ZZ+/btrUGDBtatWzfnc4sWLTKPx8PI+VcRHx+fakDBP7No0SILDg62Xr162apVq2z58uU2cOBAZ5ok77gIL7zwghUrVsynv6j34VJISIjt27cvxbav9pArI7vy5nfmzJk2Y8aMFFO09u7d22fKuuQh9LXXXrNKlSo5/XXj4+Nt8eLFVrBgQZ+pwS5fvmynTp2yvn37WrVq1VIE0Ywi+f3BmjVrbOzYsfb555/7jLb+xx9/WJ8+fSwkJMQZoHPZsmUWFhZmzz//vB04cMDMksL76tWrrVOnTta4cWOfWX3MMneLnOQzaURERKQ6KPLIkSMtMDDQ1qxZ4/NZb70lJibaRx99ZB6Px+mP/uOPP9ro0aOtfv365vF4rGDBgs5YAZmVt6VjmzZtrHXr1j7nz1WrVpnH47EuXbpctTXDwIEDrWzZss5xnZoDBw7Y888/bx6Px1544QXbunVrhjy+CemZkPfm4sknn7QuXbqYGQPDpbWDBw/6DGbhHYV8wIABFh4e7gxEVLduXXv55ZedcgkJCTZlyhTnibl3WWaQmJjojIa6ZcsW27p1q5UoUcLat2/vU+7y5cvWrFkza9y4scXExKS4eebY/tO4ceOsXbt2dujQoRT10q9fP6tcubJ99913dvHiRbtw4YJNnDjRChcubIMHD3amnho9erQFBAQ4I9TGx8fb4cOHnUGILl686HOsnz592l544QUrUaKEzyAxSOny5cscr9eBtw5fffVVK1OmTIrj7rnnnrOwsDBn5oxjx479v/buPKqqcv0D+PcwqzggmuVVcQQDURRBARUwc4KUITkOSEVXxTQz5yhNUVGLLMCkxFmcFbByQlC0UktpqTigIJpzigOiCBw4z+8Pfmdfjlr35r0CwvezVmvF2fts97vZ7P0+7/C84u3tLfb29hIZGSkbNmyQqKgocXR0lLCwsGoZkP+VpKQkcXNzExcXF3F0dFQq3bpVHLKyssTV1VWCg4OV54auYTAzM1N8fHzk7bffVhqm7t69KyEhIdKvXz+9UQrh4eFiY2MjkZGRotFoquzfxsWLFyUoKEjs7e2VJQB79eql12B/5MgRcXV1ldDQUBEpbRSNj48XGxsbcXV1lcDAQHnnnXfEyclJvL29JS0trYJKU3k9Xnd6fCm0gIAACQoKUn4ue7+VTWYWHBwsXbp0eSLx3tmzZ/mO+3/37t2TDh06yO7du0Wk9H6NiIiQdu3aSUBAgCQlJT3RWXDlyhX54YcfxNvbW2+teZ2yQfiYMWPE29tb9uzZU2WGtj8Ng/RqSqvVyqhRo0StVlfZFuqK8viL4OjRo0qFRefcuXPi6ekpq1evlilTpoiXl5ccO3ZMwsPDxdPTUy/z+MWLF2X48OFiY2Oj1/pbHeTk5Iivr68MGDBAREQiIyPFxsZGQkND5fjx43Lu3DlZvny5dOnShaNC/oLuniz7t66rdJSUlMjVq1fF0dFRyS5e1syZM8XBwUH27NkjIqVBubu7u3h5ecnatWtl9erV4ufn90Q+hLy8PDl79qx8/fXX4ujoKOHh4c+reEQiIpKeni737t0TkdIcLP7+/qJWq/XmOIuUVrQ7deoko0ePVnosjx49KtOmTVPWqndycpKIiIiKKUgldubMGfHy8pLx48fLL7/8IllZWZKamipOTk4SHBwst27dEhGRiIgI6dSpkyxbtkz5ru6ZExkZKba2tno957qpBWUD07S0tCd6NauaM2fOiFqtlsDAQPn+++/l1KlTcvHiRfHy8pKAgAAl2M7Ly5Po6Ghp27at0hgiUjq0ffbs2TJixAh59913ZfXq1XrHr6oNG3/X49dh2rRp0rt3b8nIyBCR0oYiX19fUavVSs6Vp62kIVI6QsfR0VHJkaNrxOO1/pczZ85Ijx49ZO3atZKYmChubm7i6uoqy5Ytkxs3bug1fCYlJcmsWbNk5MiR0qFDBwkODv7TnB+630NOTo7yrK/KjCo6cR2Vv+LiYly6dAnZ2dlwdHREzZo1K/qUqoSSkhIYGho+kX3S3t4eI0aMwJdffon58+cjJCQElpaWsLa2Rk5ODj799FMMGTIE69evR3FxMerUqYPffvsNffv2BQBYWVnB398ftra21S4LtqWlJd59911MnDgRW7duxbhx4yAiiI2NRWJiIho2bIibN2/Cx8cHQ4cOrejTrfR0f+vx8fH49ddfMX/+fBgYGKBGjRowNjZG48aNAQBFRUVKhvaQkBDs2LEDP/30Ezw8PGBkZISwsDBMmTIFn332GbRaLby9veHn5wegNNvq+fPnMXbsWJiamiI7OxvDhg3D5MmTK6bQVOUlJiZi2bJlKC4uxtChQ+Hn54datWqhZcuWOHbsGPLz82Fubg4TExOUlJSgadOmeOONN7Bx40a4uLhg+PDhcHR0hKOjI8aNG4cbN27AysoK9evXB1Caqfjx53pVkpubi5s3b6JNmzZKWXXvMxGBSqVSft69ezdu3bqFL774AjY2NgAACwsLGBkZ4dy5czh16hTc3d0xevRopKSk4IcffkC3bt1gbW0NjUYDExMTvPXWW+jQoQPc3d2Vc7hz5w5q1qyJ+/fvK5916tSp3K9Febty5Qry8vIwZcoUuLm5KdmwW7dujdTUVHz//fdo164dzM3N0bt3byQlJWHhwoVYtWoVVCoVXFxc4OLiAq1WC61Wq3y/uLgYRkZG1X5lnj+7Dq+//joSEhKQmpqK5s2bo169emjdujX279+PEydOwMXFBSqVSrn/DQ0Nlfeik5MTXF1dsW3bNowfPx5169YFwFWQymrbti2MjY0xb948GBgYYNCgQQgICECLFi2U1UjS09NRq1Yt3L59Gz/++COaNGmCzz77DL179wYA5dqXpasDW1palm+BKkqFNhFQhbh9+7a89dZb4uzsLKmpqRV9OlXOwYMHJTo6Wo4cOaIM+3348KGsXr1abGxsZP78+VJcXCyzZs2Sd999V0RKh7JNmDBBydaqyzSua22sLkPcn+bRo0fy3nvvSadOnfQSEK1bt06io6P1kpGxJVvf4/fN7t27ZcKECcrKALqEWFevXhW1Wi1eXl7KvlqtVrRarRQWFsro0aOlZ8+eesc6f/68JCUl6Q0Z1F1/jUYj0dHREh4eXqUTPlHFunXrlowdO1YcHBzko48+kiVLlig5K0REtmzZIjY2NsrUjLKrQmzZskXs7Ozk7bffVnrTHp/TWB2mHzx48ED8/f3Fzc1NRJ58hpadN1pSUiLBwcEyadIkZdvMmTPF1tZW1Gq1pKamSl5ennIdN2/eLJ07d5Z58+Ypx3j8+CUlJZKXlydqtVrc3Nyq3fPi9u3benNvd+3aJW5ubuLm5iYDBgyQ7t27K6OYCgsLlftWN9//adezqt+zzyIxMVFiY2Pl9OnTSj0iODhYPDw85NChQyJS2vv76quvSmhoqDIiROf48ePi6+ur/D1kZ2criWrp6TZt2iQ2NjYSHh7+xCi+o0eP6g1rf3z+eVWcX/4sGKRXU1V9HsfzcOXKFVmxYoVewqyyQdD9+/eVDNcODg7SoUMHZc6/TlhYmLi6usrXX38tp06dEjs7O2XOXlpamoSEhIiNjU21TBT3V9LS0sTZ2VmZj/e4kpKSat2Q8Z9ISEgQBwcHmTlzpmzcuFF8fX0lMDBQ2b5gwQJxcHCQ9evXi4j+MmyjRo2Sfv36PVFx0SkuLn7i+nMeLz1vmzdvFjc3N0lISJCbN28+sT0rK0uGDBkinp6ecu3aNb1toaGhyrzqxYsXl9cpV0qbNm0SOzs7WbNmjfLZrl27RK1Wi1qtlhkzZijLH02dOlW8vLwkISFBnJ2dxd3dXdatWyc5OTlKxbrstCw/Pz9xdHSUM2fO/OU5rF+/XrKzs59D6V4M+fn5MnfuXLGzs5PQ0FC5ceOGZGdnK4mxdPf35cuXJSAgQAIDAxnI/Imy76Jz587JwIEDxdbWVuzs7KRr166ycOFCESkNDHVJJHX5EXR5EKZPny6PHj2S69evy8GDB2XSpEnSt29fOXnyZIWU6UWk0WjE19dXPDw8lEalS5cuSVJSkgQGBsrAgQP1Vs4QYXD+OAbpRP+hmJgYvV4ZHY1GI4cPH5aTJ0+Kj4+PxMfHy/Hjx5Xl06Kjo5V97927p6y1GRMTI25ubrJ8+XJl++XLl8XT01MWLVpUbuV6ERQXFyvzRXUv07JzqunpSkpKJDw8XEJDQyUsLExmz56tVPYWLVokTk5OSlB+5swZ8fHxERcXF73eyPT0dOnRo4d8/PHHT/032GtD5WnHjh1KZuXx48fLkCFD9Hp7b9++LXl5eUovV1JSkjg5OYlarZaUlBTJysqSzZs3S0BAgJw7d04CAgJErVbrZdOubh48eCAjR44UJycnefDggcTHx0vnzp0lKChIhg4dKo6OjsrIuxUrVoiTk5PY2NhIVFSUZGZm6jXIXbhwQZYsWaI0ihw/flzpqaQ/l5GRId27d5dFixbJnTt3pKSkRDQajXh5eclrr72mjK4TkWo32uBZXL58Wc6fPy/r1q2T4cOHy759++Tw4cPywQcf6C3BFh4eLu3bt5ddu3Yp39V1trRv31569eolzs7O0rNnzyqfH+F5yMzMFD8/P7GxsZFu3brJgAEDxMHBQQYNGvRvG+5IRCUiUtFD7okqMykzL2bBggUYMWKEMlexsLAQn3/+OeLi4mBnZwd3d3eMGzcOQOkcu5iYGGzYsAFbt26FtbW18nlsbCxWrFiBevXqwdnZGfPnz1fmC2s0GmXODv1LTk4OAKBBgwYVfCYvjuLiYkydOhXbt29HgwYNsGrVKrRq1QoAcP78ecyaNQu3bt3C5s2bYW5ujsTERHz11VdQqVTo3r07Xn75ZRw4cACXL19GREQEXFxcnjpPjOh5S0lJQXh4OEQE9+7dw86dO7FmzRokJiZizJgxqFOnDpKTk3Hr1i1kZGTA3t4eQUFB8PT0RFJSEqZPn47c3FzUrVsXubm56NWrFxYtWoS1a9diwYIF2LFjB5o0aVLRxawwhw4dQkhICPz8/KBSqWBgYIARI0agUaNGOHbsGD755BOYmprCy8sLP/74IzIyMpCcnIxatWoBKH3WZGVlKXkqFi5cqLwn6d/btGkT5syZg2XLlsHJyQkAkJqaio8//hj5+flo0aIFYmJi0KhRI+U7uvnWpC8/Px+BgYE4ffo02rVrh1GjRuH1118HAGRlZSnvvZ07d0JE4OLiAgcHB4SGhsLKygp5eXk4e/YsUlNTkZeXhzZt2iAwMLCCS/Xiunv3LlJTU5GZmYmCggI4OTmhX79+AErr1wDn8/8ZBulE/wFd0hyd69evo2HDhjAyMsL+/fuxaNEiZGRkICoqCp6enkogc+bMGYwfPx6tWrXC4sWL9Y45btw4JCUloWnTpli+fDmaNm1a3sV6IT3+u6C/lpWVhalTp+L69euIi4uDlZWVcv02btyIqKgo+Pj4YPLkySgqKkJGRgYWLFiAixcvwtDQEFZWVvjkk0+UJFFE5W3nzp0ICwuDk5MT1Go1LC0t0bZtWxQUFCAkJAQnTpxAYWEhOnbsiAYNGsDKygrx8fGoVasW1q5dC0tLS5w6dQqZmZnIzs6Gs7MzunXrBgCIiYlBZGQk4uLi0Llz5wouacUpLi7GnDlzsHHjRlhYWGDVqlVo06aNsv3YsWMYPHgwAgMD0ahRIyxduhRt27ZFcHAwLCwskJmZifj4eFy/fh0TJ05E//79WfH+G27evIkePXrggw8+wMCBA3Ht2jUsXboUzs7OsLOzQ+3atWFra1vRp/nCSExMxJdffglDQ0PExcUpSVFFBCkpKfjwww8xduxYjBo1Cps3b8b06dMxc+ZMDB48WO84bJR+fliX+/fYBEf0Hyj7IFm0aBHi4uIQGRmJLl26wNnZGT169EB6ejru3r0LoDQbsKGhIVq1aoXAwEDMnTsX+/btg6enp9L6PWXKFDRo0ACdO3dmgP438KH+97Rq1Qp9+vTBwoULkZ6ejpYtWyr3YO/evXH48GFs27YNfn5+aNWqFdq3b4/ly5ejoKAAOTk5Ss+7VquFSqVihYXKjYhAo9Fg3bp1sLCwwLhx49C6dWsApUGlmZkZoqKicPXqVTRs2BAajQavvPIKAMDY2Bjr1q3DlStXYGlpCTs7O9jZ2SnH1mg0OHfuHPbs2YMOHTqgY8eOFVLGysLIyAjDhg3DiRMncOXKFZSUlCiZ3rVaLRwcHODl5YUDBw7giy++gJWVFebMmYNRo0ahXr16KCwshK2tLb755htl1Bj951566SWMHDkSixcvxsaNG1FUVAQzMzOMGDECjo6OABgw/h19+vTBgQMHkJycjEePHgH41yoNnTp1gre3N5YuXQofHx8MGjQI69evx/Lly2FnZwd7e3vlOLze/3u63wPrcv9e1V1ThOi/VFJSovezVqsFAPTt2xf37t3D3r17kZubixo1auC1116DnZ0dYmNjAUBZusbExAQeHh5wcnJCdHQ0ioqKlOFpTZo0wYwZM9C/f//yLRhVKyqVCv7+/ujQoQO+/vpriAiMjIyg1WphYWGBN954A+bm5oiKilK+Y2xsjLp16yoBeklJCQwMDFhhoXKlUqmQn5+PtLQ0+Pv7o3Xr1srwSN1ztE6dOnj11VfRoEEDJUD/448/kJmZiYYNGyr3cFn79+9HQkIC5syZg99//x1qtVp5ZldnrVu3Rt++fXH//n2cPn0aBgYGetdkwIABuHTpEvLy8tC7d2+sWbMGS5cuxYwZM/Dtt99i7dq1sLa2hlarrfbX8lm8//77CAsLg7u7O4KDg7F3714lQAcYMP4dNWrUwLBhw2BmZoalS5fqbatfvz7efPNNmJqaIjo6GgAwefJk5ObmKkuP0vNTlZez/F/jlSJ6jG69UV0rX1FREQAoPQqtW7fGkCFDsGXLFhw5cgQAYGtrizfffBMXLlzA8uXLleMApcF4UFAQTp8+jZUrV5Z/gajas7S0RFBQEC5fvqw0JOnuT3d3dzg6OiI5ORlnz54F8ORLlC3eVFGuX78OU1NT/PHHHwD0AxUpTX6r/Hzp0iXs2bMHX375JQ4fPgw/Pz/UrFlTb5/Dhw/j22+/RUREBIyMjLB27Vr4+fk9cezqqGyD3uLFi/HgwQMA/3oe6J4Z165dAwBYWVmhW7du6N+/P5ydnQGwQe+/YWxsDF9fX3z66af45z//CaB0xAg9GwcHB/j4+CAhIQHHjx+HgYGB0vlia2uLwYMHY8uWLUhLS4OLiwt++eUXTuuiSoVz0onK0A3DAYATJ05g1apVMDAwQJs2bdC7d280b94cQGnCOBcXF7i6uiI0NBSNGzfG5cuXER4ejiNHjiApKQn169dXjpeTk4N169bBw8MD7du3r8ASUnVVUFCAyZMn46effkJycjIsLS2VYe/nz58HgKf2OhJVtNdeew2NGzdGeHg4mjZtqvecBkqHri9btgyLFy+GhYUFjI2NMXnyZPTp0+eJY+Xn5yMjIwMqlaraD3H/M9u3b8eUKVMQEBCAESNGoHHjxrhz5w6++uor7N69G5s2bYKVlZXedzgU+3+LCbX+Ny5duoQRI0bA0tIS69at09t29uxZJCYm6iUDJqpMGKQTPSY/Px9ffPGFXkXkwoULsLGxwVdffYVmzZoBAOLi4jBnzhyEhYUhICAAAJCUlITQ0FB4e3tj5syZT1QmiSrSb7/9hjFjxqB79+5KFuay9ycr2lQZrVmzBnPnzkVoaCiGDx+ud48mJCTg8OHDmDp1KlauXImWLVvCx8dH2c5n8N9XUFCAadOmYdeuXbC1tYW9vT1yc3ORlJQEf39/fPTRR6hRowafFfRCWLlyJebPn4+IiAh4e3szKz69MPjmompNN3xP9/9FRUVYvHgx0tLSMHHiRERGRuKHH37At99+i9zcXMydO1fZPzAwEC1atMCmTZuQkZEBAHB2dsaAAQOwYcMGnDp1ipVDqlQ6dOgAT09PfPfdd/jjjz+euD9Z6abKaOjQoUpSMl1v2NWrV5GcnIyEhATcunULxsbG+PDDD5UAXTdMmM/gv8/MzAyBgYFo06YNLl26hFatWqGgoADh4eGYPXs2atasyWcFvTB8fX3RsWNHTJs2DRqNhgE6vTD49qJqSTeARFeBS05OxsmTJ2FiYoIzZ86ga9euGDRokN6a0nfv3sX+/fuRkpKiHGfGjBk4efIk9u3bh8LCQtSrVw+vv/46AgIC8NJLL5V/wYj+gqGhISZMmICffvpJb71dosrM0NAQn3/+OV5++WXMnj0b3bp1Q0hICCZOnIiioiJ8/PHHqF27NlQqlTJPnRXx/07Hjh3Rvn17PHjwAL6+vvjmm2+UBpDHk6oSVWZ169bF22+/jaCgIDba0QuFw92pWisqKkJ0dDRiY2MxYcIEjBw5Evfv30edOnUAAJmZmZgyZQqys7Ph7e2N9PR0PHz4UC9Qf++995CWloaIiAh0796dQ4bphcA1SulFc+fOHSQnJyMjIwP5+fno3r07vLy8AHCqxvOQk5MDEUHDhg0BcOoAvbj4fKAXEYN0qtK0Wi0yMzNhbW2NkpISpXelqKgIsbGxsLCwwHfffYeBAweiZ8+eaNSokfIwP3ToECZNmgRra2uMHTtWWcIqJiYGoaGhCAoKAgBcuXIFb7zxBmJiYtC1a9eKLC4RUbXDBqfni9eXiKj8sUmUqrSMjAyEhYVh27ZtSoCenJyM8+fPIzo6GnFxcahVqxYCAgKU4b+61tbt27fD3NwcU6dOhb29PYyMjGBrawsAWLhwIe7duwegdIm1X3/9lQE6EVE50uUUYQD5fPH6EhGVPwbpVKXVqlULRkZGiIqKwo4dO9CrVy+MHTsWDRs2xDvvvIPs7GzUrFkThoaGemvpFhUV4ffff0f9+vXRtm1bmJiYICcnB6tXr0bXrl1RXFyMDRs2KPsbGxtXRPGIiKotDr0mIqKqim84qtKsrKzQu3dvXLt2DZMmTYKDgwNiY2NhaWmJDz/8EHXq1EFmZiays7OhUqmUnhmVSgUbGxukp6cjPj4eP//8M9avX4+CggK8//772L59O0JCQiq4dEREREREVNUw/SlVaQ8fPsS+fftgbm6OwsJCDB06FJ06dQIAmJiY4IMPPsDs2bOxd+9etGzZEgYGBigpKYGxsTF8fX1x9epVhIaGwszMDCqVCmPHjoWjo2MFl4qIiIiIiKoqJo6jKu/ixYs4ffo0wsPD4ezsjIULF+olwunXrx8MDQ0xb9482Nvb6227f/8+fvvtN9y+fRs9e/aEhYVFRRaFiIiIiIiqOA53pyqvefPm6NWrFwYPHowdO3bgxx9/hKGhITQaDQBg+vTpyMrKwo4dO1BUVARDQ0Nl2HudOnXg4eEBf39/BuhERERERPTcMUinasHExAS9evXCq6++isjISGg0GiXbu6urKzw8PPD999/j4MGDAJiQiIiIiIiIKgYjEao2rK2tMXjwYJw8eRKbN29WEsWJCEJDQ5GTk4Nt27bh0aNHFX2qRERERERUTXFOOlUrV69exZw5c5Ceno6wsDCYmppi2bJlCAoKwsOHD9G5c2dlvXQiIiIiIqLyxiCdqp2jR49iypQpuH37NgDA3NwckZGR6Ny5cwWfGRERERERVXcM0qlays7OxoEDB/DSSy+hf//+FX06REREREREABikExEREREREVUaTBxHREREREREVEkwSCciIiIiIiKqJBikExEREREREVUSDNKJiIiIiIiIKgkG6URERERERESVBIN0IiIiIiIiokqCQToRERERERFRJcEgnYiIiIiIiKiSYJBOREREREREVEkwSCciIiIiIiKqJBikExEREREREVUSDNKJiIioytBoNCgsLKzo0yAiInpmDNKJiIjomUVHR8PGxgZXrlx5YlvPnj0xfPhw5efU1FQEBgaiS5cuaN++PTw8PDB27FhcuHBB73s3b97Ep59+Cg8PD7Rr1w7dunXD9OnTcfv27af+25mZmZg3bx569OiB9u3b49ixY8+lrEREROXBqKJPgIiIiKq+X3/9FaNHj0abNm0watQo1K5dGzdv3sShQ4dw6dIltGjRAgBw7do1qNVqaDQavPnmm2jWrBl+//13rF+/Hr/88gu2bt2K2rVr6x170qRJMDMzQ3BwMACgYcOG5V4+IiKi/xUG6URERPTcpaSkQKvVYsWKFbC0tFQ+HzNmjN5+s2fPRnFxMRITE/Hyyy8rn/ft2xdqtRorV67E+++/r/edOnXqYMWKFTAyYrWGiIhefBzuTkRERM+drvd79+7dKC4ufuo+eXl5SE1NRc+ePWFiYoI7d+4o//3jH/9As2bN8PPPPz/xvbfeeosBOhERVRl8oxEREdFzN2zYMKSkpGDWrFmIiIiAo6MjunfvDm9vb9SvXx8AcOHCBWi1WmzZsgVbtmx56nGaNm36xGfNmzd/nqdORERUrhikExER0TNTqVR/uq1sj7mFhQW2bNmCo0eP4uDBgzhy5AjmzZuH6OhoLFmyBB07doSIAAAGDBgAX1/fpx7T1NT0ic/MzMz+y1IQERFVHgzSiYiI6JnVrVsXAJCbm4smTZoonxcWFuLWrVuwsrJSPjM0NESXLl3QpUsXAEBGRgb8/f0RExODJUuWoFmzZlCpVNBoNHB1dS3fghAREVUSnJNOREREz0w31PzgwYN6n69cuRJarVb5+c6dO098t2XLljA1NUVubi6A0t52d3d37Nmz56nLqInIU49DRERUlbAnnYiIiJ6Zq6srWrRogaioKNy7dw9NmjRBWloajh8/DgsLC2W/6dOn48aNG+jWrRsaN26MgoIC7Ny5Ew8fPsTAgQOV/WbOnImhQ4ciMDAQAwcOhK2tLbRaLS5fvoyUlBT4+Pg8kd2diIioKmGQTkRERM/M0NAQMTExmDNnDuLi4mBsbAw3NzfExcVhyJAhyn4DBw5EfHw8EhIScOfOHZibm6N169aIiopCnz59lP1eeeUVbN26FbGxsdi7dy++++47mJqa4pVXXoGnpyf69etXEcUkIiIqNyrRZWkhIiIiIiIiogrFOelERERERERElQSDdCIiIiIiIqJKgkE6ERERERERUSXBIJ2IiIiIiIiokmCQTkRERERERFRJMEgnIiIiIiIiqiQYpBMRERERERFVEgzSiYiIiIiIiCoJBulERERERERElcT/ARfzZIG7frEnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = read_query(\"\"\"\n",
    "CALL gds.pageRank.stream('twitch')\n",
    "YIELD nodeId, score\n",
    "WITH nodeId, score\n",
    "ORDER BY score\n",
    "DESC LIMIT 10\n",
    "RETURN gds.util.asNode(nodeId).name as user, score\n",
    "\"\"\")\n",
    "\n",
    "fig, ax = pyplot.subplots(figsize=(16,9))\n",
    "ax = sns.barplot(x=\"user\", y=\"score\", data=data, color=\"blue\")\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "azNd5tuRcRqb"
   },
   "source": [
    "Results might vary significantly if you have scraped the Twitch API at some other times. In this analysis, chat engagement only between the 7th and the 10th of May is considered. My assumption is that streamers with the highest PageRank score are likely to have the highest count of other streamers engaging in their chat. We can easily validate this assumption by running the PageRank algorithm on the streamer subset of the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7RROKBUQcRqb",
    "outputId": "80ed6f67-b6e7-46f0-e536-f2a38b6f6495"
   },
   "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>user</th>\n",
       "      <th>score</th>\n",
       "      <th>games</th>\n",
       "      <th>stream_relationships_count</th>\n",
       "      <th>user_relationship_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>yassuo</td>\n",
       "      <td>38.614368</td>\n",
       "      <td>[Slots, League of Legends]</td>\n",
       "      <td>16</td>\n",
       "      <td>22632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>trainwreckstv</td>\n",
       "      <td>32.479834</td>\n",
       "      <td>[Slots]</td>\n",
       "      <td>101</td>\n",
       "      <td>83934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>loltyler1</td>\n",
       "      <td>19.037138</td>\n",
       "      <td>[League of Legends]</td>\n",
       "      <td>9</td>\n",
       "      <td>44564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>xqcow</td>\n",
       "      <td>12.269107</td>\n",
       "      <td>[Just Chatting, Grand Theft Auto V]</td>\n",
       "      <td>105</td>\n",
       "      <td>280443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>riotgames</td>\n",
       "      <td>12.202457</td>\n",
       "      <td>[League of Legends]</td>\n",
       "      <td>128</td>\n",
       "      <td>316735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            user      score                                games  \\\n",
       "0         yassuo  38.614368           [Slots, League of Legends]   \n",
       "1  trainwreckstv  32.479834                              [Slots]   \n",
       "2      loltyler1  19.037138                  [League of Legends]   \n",
       "3          xqcow  12.269107  [Just Chatting, Grand Theft Auto V]   \n",
       "4      riotgames  12.202457                  [League of Legends]   \n",
       "\n",
       "   stream_relationships_count  user_relationship_count  \n",
       "0                          16                    22632  \n",
       "1                         101                    83934  \n",
       "2                           9                    44564  \n",
       "3                         105                   280443  \n",
       "4                         128                   316735  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"CALL gds.pageRank.stream('twitch', {nodeLabels:['Stream']})\n",
    "YIELD nodeId, score\n",
    "WITH nodeId, score\n",
    "ORDER BY score\n",
    "DESC LIMIT 5\n",
    "MATCH (n)-[:PLAYS]->(g:Game)\n",
    "WHERE id(n)=nodeId\n",
    "RETURN n.name as user, score, \n",
    "      collect(g.name) as games,\n",
    "      count{ (n)<--(:Stream) } as stream_relationships_count, \n",
    "      count{ (n)<--(:User) } as user_relationship_count\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mLohW5VVcRqc"
   },
   "source": [
    "The top ten streamers by PageRank score are almost identical when we consider all the users or when we consider only the streamers of the Twitch network. What I found surprising is that Yassuo is in first place with only 16 inbound relationships from other streamers and 22000 relationships from regular users. I would venture a guess that the streamers who chatted in Yassuo's broadcast are themselves important by the PageRank score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rJjLQBe1cRqc",
    "outputId": "e937f62c-6cb5-4524-a72b-1d0adc4fefdd"
   },
   "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>other_streamers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[m0e_tv, benjyfishy, sanchovies, macaiyla, oda...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     other_streamers\n",
       "0  [m0e_tv, benjyfishy, sanchovies, macaiyla, oda..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "MATCH (s:Stream{name:\"yassuo\"})<--(o:Stream)\n",
    "RETURN collect(o.name) as other_streamers\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JSJqOlljcRqc"
   },
   "source": [
    "It seems that my assumption has some merit to it. Among the streamers who chatted in Yassuo's stream are loltyler1, trainwreckstv, benjyfishy. These streamers are also in the top 10 ratings by PageRank. It's not all about the number of relationships, the quality also does matter.\n",
    "## Community detection\n",
    "The last category of graph algorithms we will look at is the community detection category. Community detection or clustering algorithms are used to infer the community structure of a given network. Communities are vaguely defined as groups of nodes within a network that are more densely connected to one another than to other nodes. We could try to examine the community structure of the whole user network, but that does not make a pretty network visualization of results. First of all, we will release the existing project network from memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HKSuBbdwcRqc",
    "outputId": "7b69534c-a825-41a4-cc86-e92ebe433a6a"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>graphName</th>\n",
       "      <th>database</th>\n",
       "      <th>memoryUsage</th>\n",
       "      <th>sizeInBytes</th>\n",
       "      <th>nodeProjection</th>\n",
       "      <th>relationshipProjection</th>\n",
       "      <th>nodeQuery</th>\n",
       "      <th>relationshipQuery</th>\n",
       "      <th>nodeCount</th>\n",
       "      <th>relationshipCount</th>\n",
       "      <th>density</th>\n",
       "      <th>creationTime</th>\n",
       "      <th>modificationTime</th>\n",
       "      <th>schema</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>twitch</td>\n",
       "      <td>neo4j</td>\n",
       "      <td></td>\n",
       "      <td>-1</td>\n",
       "      <td>{'Stream': {'properties': {}, 'label': 'Stream...</td>\n",
       "      <td>{'CHATTER': {'orientation': 'NATURAL', 'aggreg...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>10514229</td>\n",
       "      <td>19723263</td>\n",
       "      <td>1.784120e-07</td>\n",
       "      <td>2021-05-21T17:13:58.338399000+02:00</td>\n",
       "      <td>2021-05-21T17:14:07.083342000+02:00</td>\n",
       "      <td>{'relationships': {'CHATTER': {}, 'VIP': {}, '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  graphName database memoryUsage  sizeInBytes  \\\n",
       "0    twitch    neo4j                       -1   \n",
       "\n",
       "                                      nodeProjection  \\\n",
       "0  {'Stream': {'properties': {}, 'label': 'Stream...   \n",
       "\n",
       "                              relationshipProjection nodeQuery  \\\n",
       "0  {'CHATTER': {'orientation': 'NATURAL', 'aggreg...      None   \n",
       "\n",
       "  relationshipQuery  nodeCount  relationshipCount       density  \\\n",
       "0              None   10514229           19723263  1.784120e-07   \n",
       "\n",
       "                          creationTime                     modificationTime  \\\n",
       "0  2021-05-21T17:13:58.338399000+02:00  2021-05-21T17:14:07.083342000+02:00   \n",
       "\n",
       "                                              schema  \n",
       "0  {'relationships': {'CHATTER': {}, 'VIP': {}, '...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.graph.drop(\"twitch\")\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BFxv_EkscRqd"
   },
   "source": [
    "I like to sometimes watch either chess or poker streamers. Let's analyze the community structure of a subgraph that contains poker and chess streamers. To ease our further queries, we will first tag relevant nodes with an additional node label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "x6QUWn3DcRqd",
    "outputId": "fc55d48b-5f1b-4041-eda3-5112340af7bf"
   },
   "outputs": [
    {
     "data": {
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     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "read_query(\"\"\"\n",
    "MATCH (s:Stream)-[:PLAYS]->(g:Game)\n",
    "WHERE g.name in [\"Chess\", \"Poker\"]\n",
    "SET s:PokerChess\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rJcEjlF6cRqd"
   },
   "source": [
    "There were a total of 63 streamers that broadcasted either chess or poker on their channel.\n",
    "If you only plan to run a single graph algorithm, you can use the anonymous graph feature, where you project and execute a graph algorithm in one step. Here, we will use the Louvain Modularity algorithm to infer the community structure of this subgraph. We will also treat this network as undirected. I would say that if a streamer engages in another streamer's chat, they are probably friends, and usually, friendship relationships go both ways. This time we will store the results of the Louvain Modularity algorithm back to the stored graph, so we can use the community structure information in our visualizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dP5gAw3ScpQN"
   },
   "outputs": [],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.graph.project('chesspoker', 'PokerChess', {ALL: {orientation:'UNDIRECTED', type:'*'}})\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OwgoX6wjcRqe",
    "outputId": "f8166154-f3b3-40c8-c927-5bc6ebd7ef9c"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>writeMillis</th>\n",
       "      <th>nodePropertiesWritten</th>\n",
       "      <th>modularity</th>\n",
       "      <th>modularities</th>\n",
       "      <th>ranLevels</th>\n",
       "      <th>communityCount</th>\n",
       "      <th>communityDistribution</th>\n",
       "      <th>postProcessingMillis</th>\n",
       "      <th>createMillis</th>\n",
       "      <th>computeMillis</th>\n",
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      ],
      "text/plain": [
       "   writeMillis  nodePropertiesWritten  modularity  \\\n",
       "0         1288                     63      0.6056   \n",
       "\n",
       "                   modularities  ranLevels  communityCount  \\\n",
       "0  [0.5552, 0.6055999999999999]          2              42   \n",
       "\n",
       "                               communityDistribution  postProcessingMillis  \\\n",
       "0  {'p99': 8, 'min': 1, 'max': 8, 'mean': 1.5, 'p...                    42   \n",
       "\n",
       "   createMillis  computeMillis  \\\n",
       "0           886            430   \n",
       "\n",
       "                                       configuration  \n",
       "0  {'maxIterations': 10, 'writeConcurrency': 4, '...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"CALL gds.louvain.write('chesspoker', {\n",
    "    writeProperty:'louvain_chesspoker'\n",
    "})\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mnsnCFKWcRqe"
   },
   "source": [
    "And now, I want to show you one last cool thing you can do in Neo4j. Instead of looking at which streamers interact with each other, we can examine which streamers share their audience. We don't have all the viewers in our database, but we have the viewers that have chatted on streamers' broadcasts. First, we will tag users who have more than a single outgoing relationship. This will help us speed up the audience comparison process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lJf4Jxh_cRqe",
    "outputId": "94a8d8d6-2bf0-4b2f-a19f-f351408a24f9"
   },
   "outputs": [
    {
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       "   batches    total  timeTaken  committedOperations  failedOperations  \\\n",
       "0       86  4276825         41              4276825                 0   \n",
       "\n",
       "   failedBatches  retries errorMessages  \\\n",
       "0              0        0            {}   \n",
       "\n",
       "                                               batch  \\\n",
       "0  {'total': 86, 'committed': 86, 'failed': 0, 'e...   \n",
       "\n",
       "                                          operations  wasTerminated  \\\n",
       "0  {'total': 4276825, 'committed': 4276825, 'fail...          False   \n",
       "\n",
       "  failedParams                                   updateStatistics  \n",
       "0           {}  {'nodesDeleted': 0, 'labelsAdded': 0, 'relatio...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"CALL apoc.periodic.iterate(\"\n",
    "    MATCH (u:User)\n",
    "    WHERE count{ (u)-->(:Stream) } > 1\n",
    "    RETURN u\",\n",
    "    \"SET u:Audiences\",\n",
    "    {batchSize:50000, parallel:true}\n",
    ")\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xuDRChnzcRqe"
   },
   "source": [
    "We need to infer a new network that depicts which streamers share their audience before we can run a community detection algorithm. To start off, we must project an in-memory graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Q2O07cMQcRqf",
    "outputId": "8e090ea6-7644-4852-bb0f-b5542feb2899"
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>{'Audience': {'properties': {}, 'label': 'Audi...</td>\n",
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       "      <td>shared-audience</td>\n",
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      "text/plain": [
       "                                      nodeProjection  \\\n",
       "0  {'Audience': {'properties': {}, 'label': 'Audi...   \n",
       "\n",
       "                              relationshipProjection        graphName  \\\n",
       "0  {'CHATTERS': {'orientation': 'REVERSE', 'aggre...  shared-audience   \n",
       "\n",
       "   nodeCount  relationshipCount  createMillis  \n",
       "0         63                 25          1030  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.graph.project('shared-audience', \n",
    "  ['PokerChess', 'Audience'],\n",
    "  {CHATTERS: {type:'*', orientation:'REVERSE'}})\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0gCiKiqpcRqf"
   },
   "source": [
    "Next, we will use the Node Similarity algorithm to infer the shared audience network. The Node Similarity algorithm uses the Jaccard similarity coefficient to compare how similar a pair of nodes are. We will assume that if two streamers share at least 5% of the audience. The mutate mode of the algorithms stores the results back to the in-memory projected graph. This was we can use the results of one algorithm as an input to another graph algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1KtHEDmScRqf",
    "outputId": "9fac2030-d9e8-469a-da90-e80c08b1e600"
   },
   "outputs": [
    {
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       "      <td>68</td>\n",
       "      <td>{'p1': 0.25, 'max': 1.0000057220458984, 'p5': ...</td>\n",
       "      <td>{'topK': 15, 'bottomK': 10, 'bottomN': 0, 'rel...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   createMillis  computeMillis  mutateMillis  postProcessingMillis  \\\n",
       "0             0             18            11                    -1   \n",
       "\n",
       "   nodesCompared  relationshipsWritten  \\\n",
       "0             20                    68   \n",
       "\n",
       "                              similarityDistribution  \\\n",
       "0  {'p1': 0.25, 'max': 1.0000057220458984, 'p5': ...   \n",
       "\n",
       "                                       configuration  \n",
       "0  {'topK': 15, 'bottomK': 10, 'bottomN': 0, 'rel...  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.nodeSimilarity.mutate('shared-audience',\n",
    " {similarityCutoff:0.05, topK:15, mutateProperty:'score', mutateRelationshipType:'SHARED_AUDIENCE', similarityMetric: 'Jaccard'})\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oR35HTxxcRqf"
   },
   "source": [
    "And finally, we can run the community detection algorithm Louvain on the shared audience network between poker and chess streamers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Hy_EjRaacRqf",
    "outputId": "bfbb22c8-8e86-46ea-b901-9652417e6d92"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>writeMillis</th>\n",
       "      <th>nodePropertiesWritten</th>\n",
       "      <th>modularity</th>\n",
       "      <th>modularities</th>\n",
       "      <th>ranLevels</th>\n",
       "      <th>communityCount</th>\n",
       "      <th>communityDistribution</th>\n",
       "      <th>postProcessingMillis</th>\n",
       "      <th>createMillis</th>\n",
       "      <th>computeMillis</th>\n",
       "      <th>configuration</th>\n",
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       "      <th>0</th>\n",
       "      <td>1036</td>\n",
       "      <td>63</td>\n",
       "      <td>0.422727</td>\n",
       "      <td>[0.4227270527894711]</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>{'p99': 4, 'min': 1, 'max': 5, 'mean': 1.21153...</td>\n",
       "      <td>10</td>\n",
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       "      <td>{'maxIterations': 10, 'writeConcurrency': 4, '...</td>\n",
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      ],
      "text/plain": [
       "   writeMillis  nodePropertiesWritten  modularity          modularities  \\\n",
       "0         1036                     63    0.422727  [0.4227270527894711]   \n",
       "\n",
       "   ranLevels  communityCount  \\\n",
       "0          1              52   \n",
       "\n",
       "                               communityDistribution  postProcessingMillis  \\\n",
       "0  {'p99': 4, 'min': 1, 'max': 5, 'mean': 1.21153...                    10   \n",
       "\n",
       "   createMillis  computeMillis  \\\n",
       "0             3             95   \n",
       "\n",
       "                                       configuration  \n",
       "0  {'maxIterations': 10, 'writeConcurrency': 4, '...  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_query(\"\"\"\n",
    "CALL gds.louvain.write('shared-audience', \n",
    "       { nodeLabels:['PokerChess'],\n",
    "         relationshipTypes:['SHARED_AUDIENCE'], \n",
    "         relationshipWeightProperty:'score',\n",
    "         writeProperty:'louvain_shared_audience'})\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "dnE1cixMcRqg"
   },
   "outputs": [],
   "source": []
  }
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