Meetup Community Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Meetup Community Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook analyses the results of applying OSLOM community finding to the Dublin meetup network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import community\n",
    "import operator, ast, re\n",
    "from collections import defaultdict\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "pd.set_option('precision', 3)\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "import pylab as plot\n",
    "%matplotlib inline\n",
    "# customise Matplotlib plots\n",
    "font_size = 14\n",
    "params = {'legend.fontsize': font_size, 'legend.handlelength': 2, 'font.size' : font_size, 'axes.titlesize' : font_size, 'ytick.labelsize' : font_size }\n",
    "plot.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "File paths and settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input files\n",
    "network_path = \"data/meetup-normalised-comembership.edges\"\n",
    "metadata_path = \"data/meetup-metadata.csv\"\n",
    "community_path = \"results/oslom-meetup.comm\"\n",
    "stopword_path = \"stopwords.txt\"\n",
    "# output files\n",
    "out_path = \"results/meetup-analysis-overall.xlsx\"\n",
    "# settings\n",
    "min_community_size = 5\n",
    "top_terms = 10\n",
    "top_categories = 3\n",
    "top_nodes = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Input & Parsing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read community memberships from a while with a list of node identifiers (assumed to be integers), one community per line. Ignore communities below a specific size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 26 communities, after filtering 6 communities of size < 5\n"
     ]
    }
   ],
   "source": [
    "communities = community.read_communities( community_path, min_community_size )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Communities cover 1466 unique nodes\n"
     ]
    }
   ],
   "source": [
    "nodes = set()\n",
    "for comm in communities:\n",
    "    for node in comm:\n",
    "        nodes.add(node)\n",
    "print(\"Communities cover %d unique nodes\" % len(nodes) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the meetup metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read metadata for 1482 meetups\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meetup_id</th>\n",
       "      <th>name</th>\n",
       "      <th>member_count</th>\n",
       "      <th>join_mode</th>\n",
       "      <th>description</th>\n",
       "      <th>created</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>370896</td>\n",
       "      <td>New and Not So New In Dublin</td>\n",
       "      <td>21149</td>\n",
       "      <td>open</td>\n",
       "      <td>Welcome to New and Not So New Dublin s largest...</td>\n",
       "      <td>2006-11-22</td>\n",
       "      <td>[Socializing]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8749202</td>\n",
       "      <td>20-35 Somethin's</td>\n",
       "      <td>5575</td>\n",
       "      <td>open</td>\n",
       "      <td>20 35 somethings is a group for people in thei...</td>\n",
       "      <td>2013-06-02</td>\n",
       "      <td>[Socializing]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19190729</td>\n",
       "      <td>Machine Learning Dublin</td>\n",
       "      <td>4422</td>\n",
       "      <td>open</td>\n",
       "      <td>This is a last Monday of the month meetup grou...</td>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>[Tech]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28444095</td>\n",
       "      <td>Data Science Festival - Dublin</td>\n",
       "      <td>688</td>\n",
       "      <td>open</td>\n",
       "      <td>The focus of this Meetup group is to provide f...</td>\n",
       "      <td>2018-05-10</td>\n",
       "      <td>[Tech]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11686062</td>\n",
       "      <td>Events, Drinks and Dancing in Dublin</td>\n",
       "      <td>16582</td>\n",
       "      <td>open</td>\n",
       "      <td>This is not a dating site but accidents do hap...</td>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>[Socializing]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    meetup_id                                  name  member_count join_mode  \\\n",
       "Id                                                                            \n",
       "1      370896          New and Not So New In Dublin         21149      open   \n",
       "2     8749202                      20-35 Somethin's          5575      open   \n",
       "3    19190729               Machine Learning Dublin          4422      open   \n",
       "4    28444095        Data Science Festival - Dublin           688      open   \n",
       "5    11686062  Events, Drinks and Dancing in Dublin         16582      open   \n",
       "\n",
       "                                          description     created  \\\n",
       "Id                                                                  \n",
       "1   Welcome to New and Not So New Dublin s largest...  2006-11-22   \n",
       "2   20 35 somethings is a group for people in thei...  2013-06-02   \n",
       "3   This is a last Monday of the month meetup grou...  2015-12-07   \n",
       "4   The focus of this Meetup group is to provide f...  2018-05-10   \n",
       "5   This is not a dating site but accidents do hap...  2014-01-02   \n",
       "\n",
       "       categories  \n",
       "Id                 \n",
       "1   [Socializing]  \n",
       "2   [Socializing]  \n",
       "3          [Tech]  \n",
       "4          [Tech]  \n",
       "5   [Socializing]  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta = pd.read_csv( metadata_path, sep = \"|\", index_col=\"Id\", converters={7:ast.literal_eval} )\n",
    "print(\"Read metadata for %d meetups\" % len(df_meta) )\n",
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Characterisation of Communities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size\n",
       "Community      \n",
       "5           216\n",
       "4           148\n",
       "7           137\n",
       "17          118\n",
       "14           84\n",
       "22           80\n",
       "3            78\n",
       "25           77\n",
       "10           71\n",
       "18           63"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for i, comm in enumerate(communities):\n",
    "    rows.append( { \"Community\" : i+1, \"Size\" : len(comm) } )\n",
    "df_comm = pd.DataFrame( rows ).set_index(\"Community\")\n",
    "df_comm.sort_values(\"Size\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate histogram of community sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = df_comm[\"Size\"].hist(figsize=(9,5), color=\"darkblue\", grid=False, bins=8)\n",
    "p.set_ylabel(\"Number of Communities\", fontsize=font_size);\n",
    "p.set_xlabel(\"Community Size\", fontsize=font_size);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at number of members in the meetups in each community:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_members, median_members = {}, {}\n",
    "for i, comm in enumerate(communities):\n",
    "    comm_member_counts = np.array( [ df_meta.loc[node][\"member_count\"] for node in comm ] )\n",
    "    mean_members[i+1] = int(comm_member_counts.mean())\n",
    "    median_members[i+1] = int(np.median(comm_member_counts))\n",
    "# add to the community data frame\n",
    "df_comm[\"Mean Members\"] = pd.Series(mean_members)\n",
    "df_comm[\"Median Members\"] = pd.Series(median_members)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Mean Members</th>\n",
       "      <th>Median Members</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "      <td>2079</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>63</td>\n",
       "      <td>1272</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>1257</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>978</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>137</td>\n",
       "      <td>915</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>53</td>\n",
       "      <td>827</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>41</td>\n",
       "      <td>671</td>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>632</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>536</td>\n",
       "      <td>236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size  Mean Members  Median Members\n",
       "Community                                    \n",
       "5           216          2079            1067\n",
       "18           63          1272             530\n",
       "9            30          1257             599\n",
       "1            30           978             559\n",
       "7           137           915             652\n",
       "3            78           890             452\n",
       "16           53           827             494\n",
       "12           41           671             313\n",
       "4           148           632             331\n",
       "6            30           536             236"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comm.sort_values(\"Mean Members\",ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the binary row-community assignment DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.zeros((len(df_meta),len(communities)), dtype=int)\n",
    "for i, comm in enumerate(communities):\n",
    "    for node in comm:\n",
    "        C[node-1,i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   C01  C02  C03  C04  C05  C06  C07  C08  C09  C10 ...   C17  C18  C19  C20  \\\n",
       "1    0    0    0    0    1    0    0    0    0    0 ...     0    0    0    0   \n",
       "2    0    0    0    0    1    0    0    0    0    0 ...     0    0    0    0   \n",
       "3    0    0    0    0    0    0    1    0    0    0 ...     0    0    0    0   \n",
       "4    0    0    0    0    0    0    1    0    0    0 ...     0    0    0    0   \n",
       "5    0    0    0    0    1    0    0    0    0    0 ...     0    1    0    0   \n",
       "\n",
       "   C21  C22  C23  C24  C25  C26  \n",
       "1    0    0    0    0    0    0  \n",
       "2    0    0    0    0    0    0  \n",
       "3    0    0    0    0    0    0  \n",
       "4    0    0    0    0    0    0  \n",
       "5    0    0    0    0    0    0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = np.arange(1, len(df_meta)+1, dtype=int)\n",
    "columns = [ \"C%02d\" % (i+1) for i in range(len(communities)) ]\n",
    "df_assign = pd.DataFrame(C, index=index, columns=columns)\n",
    "df_assign.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labelling Communities with Text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load stopwords to filter from the text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read 388 stopwords\n"
     ]
    }
   ],
   "source": [
    "stopwords = set()\n",
    "for line in open(stopword_path,\"r\").readlines():\n",
    "    stopwords.add( line.strip().lower() )\n",
    "print(\"Read %d stopwords\" % len(stopwords) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for text pre-processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "token_pattern = re.compile(r\"\\b\\w\\w+\\b\", re.U)\n",
    "def custom_tokenizer( s, min_term_length = 2 ):\n",
    "    return [x for x in token_pattern.findall(s) if (len(x) >= min_term_length and (not x.isnumeric() ) ) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess( docs ):\n",
    "    tfidf = TfidfVectorizer(stop_words=stopwords, lowercase=True, \n",
    "                            tokenizer=custom_tokenizer, strip_accents=\"unicode\", \n",
    "                            use_idf=True, norm=\"l2\", min_df = 3, ngram_range=(1,1)) \n",
    "    X = tfidf.fit_transform(docs)\n",
    "    terms = []\n",
    "    # store the vocabulary map\n",
    "    v = tfidf.vocabulary_\n",
    "    for i in range(len(v)):\n",
    "        terms.append(\"\")\n",
    "    for term in v.keys():\n",
    "        terms[ v[term] ] = term\n",
    "    return (X,terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create bag-of-words from the meetup names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_documents = list(df_meta[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meetup name document-term matrix: 1482 documents, 312 terms\n"
     ]
    }
   ],
   "source": [
    "(X,name_terms) = preprocess( name_documents )\n",
    "num_name_docs, num_name_terms = X.shape[0], X.shape[1]\n",
    "print( \"Meetup name document-term matrix: %d documents, %d terms\" % (num_name_docs, num_name_terms) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create bag-of-words from the meetup descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meetup_id</th>\n",
       "      <th>name</th>\n",
       "      <th>member_count</th>\n",
       "      <th>join_mode</th>\n",
       "      <th>description</th>\n",
       "      <th>created</th>\n",
       "      <th>categories</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [meetup_id, name, member_count, join_mode, description, created, categories]\n",
       "Index: []"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta[pd.isnull(df_meta).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "desc_documents = list(df_meta[\"description\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Meetup description document-term matrix: 1482 documents, 3945 terms\n"
     ]
    }
   ],
   "source": [
    "(D,desc_terms) = preprocess( desc_documents )\n",
    "num_desc_docs, num_desc_terms = D.shape[0], D.shape[1]\n",
    "print( \"Meetup description document-term matrix: %d documents, %d terms\" % (num_desc_docs, num_desc_terms) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each community, identify the top terms from the names and descriptions of the meetups assigned to that community:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "name_labels, desc_labels = {}, {}\n",
    "for i, comm in enumerate(communities):\n",
    "    # Compute the centroid vectors for both BoW models\n",
    "    name_centroid = np.zeros( num_name_terms )\n",
    "    desc_centroid = np.zeros( num_desc_terms )\n",
    "    for node in comm:\n",
    "        row = node - 1\n",
    "        name_centroid += X[row,:]\n",
    "        desc_centroid += D[row,:]\n",
    "    name_centroid /= len(comm)\n",
    "    desc_centroid /= len(comm)\n",
    "    # Rank the name terms\n",
    "    top_name_indices = np.argsort( name_centroid ).tolist()[0][::-1]\n",
    "    top_name_terms = [ name_terms[i] for i in top_name_indices[0:top_terms] ]\n",
    "    name_labels[i+1] = \", \".join( top_name_terms )\n",
    "    # Rank the description terms\n",
    "    top_desc_indices = np.argsort( desc_centroid ).tolist()[0][::-1]\n",
    "    top_desc_terms = [ desc_terms[i] for i in top_desc_indices[0:top_terms] ]    \n",
    "    desc_labels[i+1] = \", \".join( top_desc_terms )    \n",
    "    \n",
    "df_comm[\"Top %d Name Terms\" % top_terms ] = pd.Series(name_labels)\n",
    "df_comm[\"Top %d Description Terms\" % top_terms ] = pd.Series(desc_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Mean Members</th>\n",
       "      <th>Median Members</th>\n",
       "      <th>Top 10 Name Terms</th>\n",
       "      <th>Top 10 Description Terms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>978</td>\n",
       "      <td>559</td>\n",
       "      <td>lgbt, gay, photography, social, food, rock, ac...</td>\n",
       "      <td>gay, lgbt, geeks, chat, social, different, dri...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>327</td>\n",
       "      <td>224</td>\n",
       "      <td>blockchain, crypto, tallaght, development, tec...</td>\n",
       "      <td>blockchain, crypto, bitcoin, ethereum, technol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>452</td>\n",
       "      <td>startup, entrepreneurs, digital, lean, busines...</td>\n",
       "      <td>business, entrepreneurs, marketing, startup, n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>632</td>\n",
       "      <td>331</td>\n",
       "      <td>meditation, yoga, healing, spiritual, heart, s...</td>\n",
       "      <td>healing, life, meditation, experience, self, e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "      <td>2079</td>\n",
       "      <td>1067</td>\n",
       "      <td>hiking, international, wicklow, friends, yoga,...</td>\n",
       "      <td>fun, members, friends, time, hikes, free, soci...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size  Mean Members  Median Members  \\\n",
       "Community                                       \n",
       "1            30           978             559   \n",
       "2            28           327             224   \n",
       "3            78           890             452   \n",
       "4           148           632             331   \n",
       "5           216          2079            1067   \n",
       "\n",
       "                                           Top 10 Name Terms  \\\n",
       "Community                                                      \n",
       "1          lgbt, gay, photography, social, food, rock, ac...   \n",
       "2          blockchain, crypto, tallaght, development, tec...   \n",
       "3          startup, entrepreneurs, digital, lean, busines...   \n",
       "4          meditation, yoga, healing, spiritual, heart, s...   \n",
       "5          hiking, international, wicklow, friends, yoga,...   \n",
       "\n",
       "                                    Top 10 Description Terms  \n",
       "Community                                                     \n",
       "1          gay, lgbt, geeks, chat, social, different, dri...  \n",
       "2          blockchain, crypto, bitcoin, ethereum, technol...  \n",
       "3          business, entrepreneurs, marketing, startup, n...  \n",
       "4          healing, life, meditation, experience, self, e...  \n",
       "5          fun, members, friends, time, hikes, free, soci...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labelling Communities by Categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the overall category counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_cat_counts = defaultdict(int)\n",
    "for cat_list in df_meta[\"categories\"]:\n",
    "    for cat in cat_list:\n",
    "        global_cat_counts[cat] += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tech                      338\n",
       "Health & Wellbeing        229\n",
       "Support                   229\n",
       "Career & Business         148\n",
       "Religion & Beliefs        108\n",
       "New Age & Spirituality    108\n",
       "Paranormal                108\n",
       "Fitness                    99\n",
       "Sports & Recreation        99\n",
       "Socializing                68\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_global_cat_counts = pd.Series(global_cat_counts)\n",
    "s_global_cat_counts.sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get top precision, recall and F1 for community-category pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_dict_top( d, top ):\n",
    "    sx = sorted(d.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    values = []\n",
    "    for i in range( min(3,len(sx)) ):\n",
    "        values.append( \"%s (%.2f)\" % ( sx[i][0], sx[i][1] ) )\n",
    "    return \", \".join( values )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "comm_precision, comm_recall, comm_f1 = {}, {}, {}\n",
    "for i, comm in enumerate(communities):\n",
    "    comm_cats = defaultdict(int)\n",
    "    # get the category counts for this community\n",
    "    for node in comm:\n",
    "        for cat in df_meta.loc[node][\"categories\"]:\n",
    "            comm_cats[cat] += 1\n",
    "    # calculate precision, recall and f1\n",
    "    precision, recall, f1 = {}, {}, {}\n",
    "    for cat in comm_cats:\n",
    "        precision[cat] = float(comm_cats[cat]) / len(comm)\n",
    "        recall[cat] = float(comm_cats[cat]) / global_cat_counts[cat]\n",
    "        f1[cat] = 2*((precision[cat]*recall[cat])/(precision[cat]+recall[cat]))\n",
    "    # get the top elements\n",
    "    comm_precision[i+1] = select_dict_top( precision, top_categories )\n",
    "    comm_recall[i+1] = select_dict_top( recall, top_categories )\n",
    "    comm_f1[i+1] = select_dict_top( f1, top_categories )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add to the community data frame\n",
    "df_comm[\"Top %d Category Precision\" % top_categories] = pd.Series(comm_precision)\n",
    "df_comm[\"Top %d Category Recall\" % top_categories] = pd.Series(comm_recall)\n",
    "df_comm[\"Top %d Category F1\" % top_categories] = pd.Series(comm_f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Mean Members</th>\n",
       "      <th>Median Members</th>\n",
       "      <th>Top 10 Name Terms</th>\n",
       "      <th>Top 10 Description Terms</th>\n",
       "      <th>Top 3 Category Precision</th>\n",
       "      <th>Top 3 Category Recall</th>\n",
       "      <th>Top 3 Category F1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>978</td>\n",
       "      <td>559</td>\n",
       "      <td>lgbt, gay, photography, social, food, rock, ac...</td>\n",
       "      <td>gay, lgbt, geeks, chat, social, different, dri...</td>\n",
       "      <td>LGBT (0.47), Photography (0.10), Fitness (0.07)</td>\n",
       "      <td>LGBT (1.00), Photography (0.30), Book Clubs (0...</td>\n",
       "      <td>LGBT (0.64), Photography (0.15), Music (0.05)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>327</td>\n",
       "      <td>224</td>\n",
       "      <td>blockchain, crypto, tallaght, development, tec...</td>\n",
       "      <td>blockchain, crypto, bitcoin, ethereum, technol...</td>\n",
       "      <td>Tech (0.93), Career &amp; Business (0.07)</td>\n",
       "      <td>Tech (0.08), Career &amp; Business (0.01)</td>\n",
       "      <td>Tech (0.14), Career &amp; Business (0.02)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>452</td>\n",
       "      <td>startup, entrepreneurs, digital, lean, busines...</td>\n",
       "      <td>business, entrepreneurs, marketing, startup, n...</td>\n",
       "      <td>Career &amp; Business (0.54), Tech (0.37), Educati...</td>\n",
       "      <td>Career &amp; Business (0.28), Education &amp; Learning...</td>\n",
       "      <td>Career &amp; Business (0.37), Tech (0.14), Educati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>632</td>\n",
       "      <td>331</td>\n",
       "      <td>meditation, yoga, healing, spiritual, heart, s...</td>\n",
       "      <td>healing, life, meditation, experience, self, e...</td>\n",
       "      <td>Health &amp; Wellbeing (0.51), Support (0.51), New...</td>\n",
       "      <td>New Age &amp; Spirituality (0.45), Paranormal (0.4...</td>\n",
       "      <td>Health &amp; Wellbeing (0.40), Support (0.40), New...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "      <td>2079</td>\n",
       "      <td>1067</td>\n",
       "      <td>hiking, international, wicklow, friends, yoga,...</td>\n",
       "      <td>fun, members, friends, time, hikes, free, soci...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.16), Outdoors &amp; Adventur...</td>\n",
       "      <td>Photography (0.60), Parents &amp; Family (0.60), C...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.25), Outdoors &amp; Adventur...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size  Mean Members  Median Members  \\\n",
       "Community                                       \n",
       "1            30           978             559   \n",
       "2            28           327             224   \n",
       "3            78           890             452   \n",
       "4           148           632             331   \n",
       "5           216          2079            1067   \n",
       "\n",
       "                                           Top 10 Name Terms  \\\n",
       "Community                                                      \n",
       "1          lgbt, gay, photography, social, food, rock, ac...   \n",
       "2          blockchain, crypto, tallaght, development, tec...   \n",
       "3          startup, entrepreneurs, digital, lean, busines...   \n",
       "4          meditation, yoga, healing, spiritual, heart, s...   \n",
       "5          hiking, international, wicklow, friends, yoga,...   \n",
       "\n",
       "                                    Top 10 Description Terms  \\\n",
       "Community                                                      \n",
       "1          gay, lgbt, geeks, chat, social, different, dri...   \n",
       "2          blockchain, crypto, bitcoin, ethereum, technol...   \n",
       "3          business, entrepreneurs, marketing, startup, n...   \n",
       "4          healing, life, meditation, experience, self, e...   \n",
       "5          fun, members, friends, time, hikes, free, soci...   \n",
       "\n",
       "                                    Top 3 Category Precision  \\\n",
       "Community                                                      \n",
       "1            LGBT (0.47), Photography (0.10), Fitness (0.07)   \n",
       "2                      Tech (0.93), Career & Business (0.07)   \n",
       "3          Career & Business (0.54), Tech (0.37), Educati...   \n",
       "4          Health & Wellbeing (0.51), Support (0.51), New...   \n",
       "5          Cars & Motorcycles (0.16), Outdoors & Adventur...   \n",
       "\n",
       "                                       Top 3 Category Recall  \\\n",
       "Community                                                      \n",
       "1          LGBT (1.00), Photography (0.30), Book Clubs (0...   \n",
       "2                      Tech (0.08), Career & Business (0.01)   \n",
       "3          Career & Business (0.28), Education & Learning...   \n",
       "4          New Age & Spirituality (0.45), Paranormal (0.4...   \n",
       "5          Photography (0.60), Parents & Family (0.60), C...   \n",
       "\n",
       "                                           Top 3 Category F1  \n",
       "Community                                                     \n",
       "1              LGBT (0.64), Photography (0.15), Music (0.05)  \n",
       "2                      Tech (0.14), Career & Business (0.02)  \n",
       "3          Career & Business (0.37), Tech (0.14), Educati...  \n",
       "4          Health & Wellbeing (0.40), Support (0.40), New...  \n",
       "5          Cars & Motorcycles (0.25), Outdoors & Adventur...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analysis of Community Overlap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count communities per node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean communities per node = 1.15\n"
     ]
    }
   ],
   "source": [
    "node_community_counts = defaultdict(int)\n",
    "for comm in communities:\n",
    "    for node in comm:\n",
    "        node_community_counts[node] += 1\n",
    "s_community_counts = pd.Series( node_community_counts, dtype=int )\n",
    "print(\"Mean communities per node = %.2f\" % s_community_counts.mean() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many nodes in more than one community?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len( s_community_counts[s_community_counts > 1] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1269\n",
       "2     176\n",
       "3      20\n",
       "4       1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_counts = s_community_counts.value_counts()\n",
    "value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    86.562\n",
       "2    12.005\n",
       "3     1.364\n",
       "4     0.068\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_counts = 100 * ( value_counts/len(nodes) )\n",
    "per_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the most overlapping nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meetup_id</th>\n",
       "      <th>name</th>\n",
       "      <th>member_count</th>\n",
       "      <th>join_mode</th>\n",
       "      <th>description</th>\n",
       "      <th>created</th>\n",
       "      <th>categories</th>\n",
       "      <th>Communities</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>19140096</td>\n",
       "      <td>Headless Awareness Dublin</td>\n",
       "      <td>70</td>\n",
       "      <td>open</td>\n",
       "      <td>This group is about waking up to a new level o...</td>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>[New Age &amp; Spirituality, Paranormal, Religion ...</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>18377859</td>\n",
       "      <td>Dublin Yoga for Everyone</td>\n",
       "      <td>2863</td>\n",
       "      <td>open</td>\n",
       "      <td>Yoga is a process and a journey that can help ...</td>\n",
       "      <td>2015-01-25</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>18802268</td>\n",
       "      <td>GAELCON 2017</td>\n",
       "      <td>60</td>\n",
       "      <td>open</td>\n",
       "      <td>The best 4 days in Irish gaming We ll have a m...</td>\n",
       "      <td>2015-08-04</td>\n",
       "      <td>[Games, Sci-Fi &amp; Fantasy]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>24114196</td>\n",
       "      <td>Yoga with Miriam</td>\n",
       "      <td>30</td>\n",
       "      <td>open</td>\n",
       "      <td>This is yoga for everybody all shapes all size...</td>\n",
       "      <td>2017-05-29</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>18709373</td>\n",
       "      <td>Dublin Foosball</td>\n",
       "      <td>497</td>\n",
       "      <td>open</td>\n",
       "      <td>Do you like play foosball chat listen to some ...</td>\n",
       "      <td>2015-06-30</td>\n",
       "      <td>[Games, Sci-Fi &amp; Fantasy]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>21985109</td>\n",
       "      <td>Dublin Nightlife</td>\n",
       "      <td>2361</td>\n",
       "      <td>open</td>\n",
       "      <td>The whole purpose of Dublin Nightlife is Based...</td>\n",
       "      <td>2017-01-19</td>\n",
       "      <td>[Socializing]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>533</th>\n",
       "      <td>27058744</td>\n",
       "      <td>Acting for Camera Course</td>\n",
       "      <td>360</td>\n",
       "      <td>open</td>\n",
       "      <td>IF YOU ARE INTERESTED IN ACTING FOR SCREEN THI...</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>[Movies &amp; Film]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>18305854</td>\n",
       "      <td>How to use your Camera Photography Meetup</td>\n",
       "      <td>5267</td>\n",
       "      <td>open</td>\n",
       "      <td>Hi can I give you a warm welcome to this group...</td>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>[Photography]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>26251755</td>\n",
       "      <td>Electronic Music &amp; Food Sessions</td>\n",
       "      <td>1104</td>\n",
       "      <td>open</td>\n",
       "      <td>Let s make our love for Electronic music i e T...</td>\n",
       "      <td>2017-10-13</td>\n",
       "      <td>[Music]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>10521812</td>\n",
       "      <td>Radical Forgiveness: Empowerment and Transform...</td>\n",
       "      <td>31</td>\n",
       "      <td>approval</td>\n",
       "      <td>Join a monthly Meetup group to explore Colin T...</td>\n",
       "      <td>2013-10-02</td>\n",
       "      <td>[New Age &amp; Spirituality, Paranormal, Religion ...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>1627895</td>\n",
       "      <td>HEALING MEDITATION &amp; YOGA-DANCE</td>\n",
       "      <td>3912</td>\n",
       "      <td>open</td>\n",
       "      <td>Step into a heart centered circle for inspirin...</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>26809419</td>\n",
       "      <td>Rock and Roll 🎸 Meet and Greet</td>\n",
       "      <td>1168</td>\n",
       "      <td>open</td>\n",
       "      <td>Let s get together for Rock and Roll concerts ...</td>\n",
       "      <td>2017-12-07</td>\n",
       "      <td>[Music]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>23243950</td>\n",
       "      <td>Dublin Photography Studio Lighting workshop Me...</td>\n",
       "      <td>1118</td>\n",
       "      <td>open</td>\n",
       "      <td>This one day workshop is for anyone interested...</td>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>[Photography]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>22269777</td>\n",
       "      <td>ANAMCHARA - Prayer Support Group</td>\n",
       "      <td>39</td>\n",
       "      <td>approval</td>\n",
       "      <td>Welcome Blessing May today there be peace with...</td>\n",
       "      <td>2017-02-07</td>\n",
       "      <td>[New Age &amp; Spirituality, Paranormal, Religion ...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>23841918</td>\n",
       "      <td>Building Resilience Meditation Workshop</td>\n",
       "      <td>34</td>\n",
       "      <td>open</td>\n",
       "      <td>The workshop is open to everyone There is no n...</td>\n",
       "      <td>2017-05-18</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>24244725</td>\n",
       "      <td>Ki Yoga Massage</td>\n",
       "      <td>32</td>\n",
       "      <td>open</td>\n",
       "      <td>This is a group for anyone interested in Compl...</td>\n",
       "      <td>2017-06-03</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>27153278</td>\n",
       "      <td>Qi Gong for your health and potential</td>\n",
       "      <td>76</td>\n",
       "      <td>open</td>\n",
       "      <td>Using simple Taoist and Shaolin Qi Gong techni...</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1238</th>\n",
       "      <td>28193963</td>\n",
       "      <td>Lose Weight and Gain Health: The Mcdougall Sta...</td>\n",
       "      <td>67</td>\n",
       "      <td>open</td>\n",
       "      <td>A bi weekly meetup for people looking to learn...</td>\n",
       "      <td>2018-04-17</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1369</th>\n",
       "      <td>29395409</td>\n",
       "      <td>Ambrosus Dublin</td>\n",
       "      <td>90</td>\n",
       "      <td>open</td>\n",
       "      <td>Originally founded in Switzerland Ambrosus com...</td>\n",
       "      <td>2018-07-30</td>\n",
       "      <td>[Tech]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>22218604</td>\n",
       "      <td>Dublin Rangefinder Photography  Meetup</td>\n",
       "      <td>760</td>\n",
       "      <td>open</td>\n",
       "      <td>We are photographers that prefer the manual wo...</td>\n",
       "      <td>2017-02-03</td>\n",
       "      <td>[Photography]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1159</th>\n",
       "      <td>26986295</td>\n",
       "      <td>Seekers of Truth &amp; Self Knowledge</td>\n",
       "      <td>34</td>\n",
       "      <td>open</td>\n",
       "      <td>Welcoming people from all spiritual traditions...</td>\n",
       "      <td>2017-12-28</td>\n",
       "      <td>[New Age &amp; Spirituality, Paranormal, Religion ...</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>23395014</td>\n",
       "      <td>Passive Income Incubator Dublin</td>\n",
       "      <td>260</td>\n",
       "      <td>open</td>\n",
       "      <td>Hi My name is Laura and I have recently become...</td>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>[Career &amp; Business]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>19086459</td>\n",
       "      <td>Dublin Holistic  Body and Soul  Meetup</td>\n",
       "      <td>1568</td>\n",
       "      <td>open</td>\n",
       "      <td>This group is for everyone interested in spiri...</td>\n",
       "      <td>2015-11-03</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1577440</td>\n",
       "      <td>POSITIVE LIVING NETWORK DUBLIN</td>\n",
       "      <td>4735</td>\n",
       "      <td>open</td>\n",
       "      <td>This growing community is organised in Dublin ...</td>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>[Health &amp; Wellbeing, Support]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>18511013</td>\n",
       "      <td>Tuesday Irish Dancing Class: Set Dancing, Sean...</td>\n",
       "      <td>2586</td>\n",
       "      <td>open</td>\n",
       "      <td>Who We are a group of people who love to dance...</td>\n",
       "      <td>2015-03-19</td>\n",
       "      <td>[Dancing]</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      meetup_id                                               name  \\\n",
       "Id                                                                   \n",
       "1346   19140096                          Headless Awareness Dublin   \n",
       "311    18377859                           Dublin Yoga for Everyone   \n",
       "609    18802268                                       GAELCON 2017   \n",
       "876    24114196                                   Yoga with Miriam   \n",
       "341    18709373                                    Dublin Foosball   \n",
       "193    21985109                                   Dublin Nightlife   \n",
       "533    27058744                           Acting for Camera Course   \n",
       "342    18305854          How to use your Camera Photography Meetup   \n",
       "317    26251755                   Electronic Music & Food Sessions   \n",
       "670    10521812  Radical Forgiveness: Empowerment and Transform...   \n",
       "217     1627895                    HEALING MEDITATION & YOGA-DANCE   \n",
       "174    26809419                     Rock and Roll 🎸 Meet and Greet   \n",
       "364    23243950  Dublin Photography Studio Lighting workshop Me...   \n",
       "683    22269777                   ANAMCHARA - Prayer Support Group   \n",
       "598    23841918            Building Resilience Meditation Workshop   \n",
       "581    24244725                                    Ki Yoga Massage   \n",
       "1118   27153278              Qi Gong for your health and potential   \n",
       "1238   28193963  Lose Weight and Gain Health: The Mcdougall Sta...   \n",
       "1369   29395409                                    Ambrosus Dublin   \n",
       "287    22218604             Dublin Rangefinder Photography  Meetup   \n",
       "1159   26986295                  Seekers of Truth & Self Knowledge   \n",
       "616    23395014                    Passive Income Incubator Dublin   \n",
       "249    19086459             Dublin Holistic  Body and Soul  Meetup   \n",
       "252     1577440                     POSITIVE LIVING NETWORK DUBLIN   \n",
       "245    18511013  Tuesday Irish Dancing Class: Set Dancing, Sean...   \n",
       "\n",
       "      member_count join_mode  \\\n",
       "Id                             \n",
       "1346            70      open   \n",
       "311           2863      open   \n",
       "609             60      open   \n",
       "876             30      open   \n",
       "341            497      open   \n",
       "193           2361      open   \n",
       "533            360      open   \n",
       "342           5267      open   \n",
       "317           1104      open   \n",
       "670             31  approval   \n",
       "217           3912      open   \n",
       "174           1168      open   \n",
       "364           1118      open   \n",
       "683             39  approval   \n",
       "598             34      open   \n",
       "581             32      open   \n",
       "1118            76      open   \n",
       "1238            67      open   \n",
       "1369            90      open   \n",
       "287            760      open   \n",
       "1159            34      open   \n",
       "616            260      open   \n",
       "249           1568      open   \n",
       "252           4735      open   \n",
       "245           2586      open   \n",
       "\n",
       "                                            description     created  \\\n",
       "Id                                                                    \n",
       "1346  This group is about waking up to a new level o...  2015-11-20   \n",
       "311   Yoga is a process and a journey that can help ...  2015-01-25   \n",
       "609   The best 4 days in Irish gaming We ll have a m...  2015-08-04   \n",
       "876   This is yoga for everybody all shapes all size...  2017-05-29   \n",
       "341   Do you like play foosball chat listen to some ...  2015-06-30   \n",
       "193   The whole purpose of Dublin Nightlife is Based...  2017-01-19   \n",
       "533   IF YOU ARE INTERESTED IN ACTING FOR SCREEN THI...  2018-01-05   \n",
       "342   Hi can I give you a warm welcome to this group...  2015-01-06   \n",
       "317   Let s make our love for Electronic music i e T...  2017-10-13   \n",
       "670   Join a monthly Meetup group to explore Colin T...  2013-10-02   \n",
       "217   Step into a heart centered circle for inspirin...  2010-04-09   \n",
       "174   Let s get together for Rock and Roll concerts ...  2017-12-07   \n",
       "364   This one day workshop is for anyone interested...  2017-04-11   \n",
       "683   Welcome Blessing May today there be peace with...  2017-02-07   \n",
       "598   The workshop is open to everyone There is no n...  2017-05-18   \n",
       "581   This is a group for anyone interested in Compl...  2017-06-03   \n",
       "1118  Using simple Taoist and Shaolin Qi Gong techni...  2018-01-14   \n",
       "1238  A bi weekly meetup for people looking to learn...  2018-04-17   \n",
       "1369  Originally founded in Switzerland Ambrosus com...  2018-07-30   \n",
       "287   We are photographers that prefer the manual wo...  2017-02-03   \n",
       "1159  Welcoming people from all spiritual traditions...  2017-12-28   \n",
       "616   Hi My name is Laura and I have recently become...  2017-04-23   \n",
       "249   This group is for everyone interested in spiri...  2015-11-03   \n",
       "252   This growing community is organised in Dublin ...  2010-01-05   \n",
       "245   Who We are a group of people who love to dance...  2015-03-19   \n",
       "\n",
       "                                             categories  Communities  \n",
       "Id                                                                    \n",
       "1346  [New Age & Spirituality, Paranormal, Religion ...          4.0  \n",
       "311                       [Health & Wellbeing, Support]          3.0  \n",
       "609                           [Games, Sci-Fi & Fantasy]          3.0  \n",
       "876                       [Health & Wellbeing, Support]          3.0  \n",
       "341                           [Games, Sci-Fi & Fantasy]          3.0  \n",
       "193                                       [Socializing]          3.0  \n",
       "533                                     [Movies & Film]          3.0  \n",
       "342                                       [Photography]          3.0  \n",
       "317                                             [Music]          3.0  \n",
       "670   [New Age & Spirituality, Paranormal, Religion ...          3.0  \n",
       "217                       [Health & Wellbeing, Support]          3.0  \n",
       "174                                             [Music]          3.0  \n",
       "364                                       [Photography]          3.0  \n",
       "683   [New Age & Spirituality, Paranormal, Religion ...          3.0  \n",
       "598                       [Health & Wellbeing, Support]          3.0  \n",
       "581                       [Health & Wellbeing, Support]          3.0  \n",
       "1118                      [Health & Wellbeing, Support]          3.0  \n",
       "1238                      [Health & Wellbeing, Support]          3.0  \n",
       "1369                                             [Tech]          3.0  \n",
       "287                                       [Photography]          3.0  \n",
       "1159  [New Age & Spirituality, Paranormal, Religion ...          3.0  \n",
       "616                                 [Career & Business]          2.0  \n",
       "249                       [Health & Wellbeing, Support]          2.0  \n",
       "252                       [Health & Wellbeing, Support]          2.0  \n",
       "245                                           [Dancing]          2.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta[\"Communities\"] = s_community_counts\n",
    "df_meta.sort_values(\"Communities\",ascending=False).head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot overal communities per node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = per_counts.plot(kind=\"bar\", fontsize=font_size, figsize=(6,4), color=\"darkgreen\")\n",
    "p.set_xlabel(\"Number of Communities per Node\", fontsize=font_size);\n",
    "p.set_ylabel(\"% of Nodes\", fontsize=font_size);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the level of overlap in each community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>% Unique Nodes</th>\n",
       "      <th>Mean Node Overlap</th>\n",
       "      <th>Median Node Overlap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53.33</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60.71</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65.38</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>87.84</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>83.33</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           % Unique Nodes  Mean Node Overlap  Median Node Overlap\n",
       "Community                                                        \n",
       "1                   53.33               1.70                    1\n",
       "2                   60.71               1.43                    1\n",
       "3                   65.38               1.35                    1\n",
       "4                   87.84               1.14                    1\n",
       "5                   83.33               1.19                    1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for i, comm in enumerate(communities):\n",
    "    overlaps = []\n",
    "    for node in comm:\n",
    "        overlaps.append( node_community_counts[node] )\n",
    "    unique_nodes = overlaps.count( 1 )/len(comm)\n",
    "    overlaps = np.array(overlaps)\n",
    "    rows.append( { \"Community\" : i+1, \n",
    "                  \"% Unique Nodes\" : round(100.0*unique_nodes,2),\n",
    "                  \"Mean Node Overlap\" : round(overlaps.mean(),2), \n",
    "                  \"Median Node Overlap\": int(np.median(overlaps)) } )\n",
    "df_overlap = pd.DataFrame( rows ).set_index(\"Community\")\n",
    "df_overlap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Top 10 Description Terms</th>\n",
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       "      <th>Mean Node Overlap</th>\n",
       "      <th>Median Node Overlap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <td>30</td>\n",
       "      <td>978</td>\n",
       "      <td>559</td>\n",
       "      <td>lgbt, gay, photography, social, food, rock, ac...</td>\n",
       "      <td>gay, lgbt, geeks, chat, social, different, dri...</td>\n",
       "      <td>LGBT (0.47), Photography (0.10), Fitness (0.07)</td>\n",
       "      <td>LGBT (1.00), Photography (0.30), Book Clubs (0...</td>\n",
       "      <td>LGBT (0.64), Photography (0.15), Music (0.05)</td>\n",
       "      <td>53.33</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>327</td>\n",
       "      <td>224</td>\n",
       "      <td>blockchain, crypto, tallaght, development, tec...</td>\n",
       "      <td>blockchain, crypto, bitcoin, ethereum, technol...</td>\n",
       "      <td>Tech (0.93), Career &amp; Business (0.07)</td>\n",
       "      <td>Tech (0.08), Career &amp; Business (0.01)</td>\n",
       "      <td>Tech (0.14), Career &amp; Business (0.02)</td>\n",
       "      <td>60.71</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>452</td>\n",
       "      <td>startup, entrepreneurs, digital, lean, busines...</td>\n",
       "      <td>business, entrepreneurs, marketing, startup, n...</td>\n",
       "      <td>Career &amp; Business (0.54), Tech (0.37), Educati...</td>\n",
       "      <td>Career &amp; Business (0.28), Education &amp; Learning...</td>\n",
       "      <td>Career &amp; Business (0.37), Tech (0.14), Educati...</td>\n",
       "      <td>65.38</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>632</td>\n",
       "      <td>331</td>\n",
       "      <td>meditation, yoga, healing, spiritual, heart, s...</td>\n",
       "      <td>healing, life, meditation, experience, self, e...</td>\n",
       "      <td>Health &amp; Wellbeing (0.51), Support (0.51), New...</td>\n",
       "      <td>New Age &amp; Spirituality (0.45), Paranormal (0.4...</td>\n",
       "      <td>Health &amp; Wellbeing (0.40), Support (0.40), New...</td>\n",
       "      <td>87.84</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "      <td>2079</td>\n",
       "      <td>1067</td>\n",
       "      <td>hiking, international, wicklow, friends, yoga,...</td>\n",
       "      <td>fun, members, friends, time, hikes, free, soci...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.16), Outdoors &amp; Adventur...</td>\n",
       "      <td>Photography (0.60), Parents &amp; Family (0.60), C...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.25), Outdoors &amp; Adventur...</td>\n",
       "      <td>83.33</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "           Size  Mean Members  Median Members  \\\n",
       "Community                                       \n",
       "1            30           978             559   \n",
       "2            28           327             224   \n",
       "3            78           890             452   \n",
       "4           148           632             331   \n",
       "5           216          2079            1067   \n",
       "\n",
       "                                           Top 10 Name Terms  \\\n",
       "Community                                                      \n",
       "1          lgbt, gay, photography, social, food, rock, ac...   \n",
       "2          blockchain, crypto, tallaght, development, tec...   \n",
       "3          startup, entrepreneurs, digital, lean, busines...   \n",
       "4          meditation, yoga, healing, spiritual, heart, s...   \n",
       "5          hiking, international, wicklow, friends, yoga,...   \n",
       "\n",
       "                                    Top 10 Description Terms  \\\n",
       "Community                                                      \n",
       "1          gay, lgbt, geeks, chat, social, different, dri...   \n",
       "2          blockchain, crypto, bitcoin, ethereum, technol...   \n",
       "3          business, entrepreneurs, marketing, startup, n...   \n",
       "4          healing, life, meditation, experience, self, e...   \n",
       "5          fun, members, friends, time, hikes, free, soci...   \n",
       "\n",
       "                                    Top 3 Category Precision  \\\n",
       "Community                                                      \n",
       "1            LGBT (0.47), Photography (0.10), Fitness (0.07)   \n",
       "2                      Tech (0.93), Career & Business (0.07)   \n",
       "3          Career & Business (0.54), Tech (0.37), Educati...   \n",
       "4          Health & Wellbeing (0.51), Support (0.51), New...   \n",
       "5          Cars & Motorcycles (0.16), Outdoors & Adventur...   \n",
       "\n",
       "                                       Top 3 Category Recall  \\\n",
       "Community                                                      \n",
       "1          LGBT (1.00), Photography (0.30), Book Clubs (0...   \n",
       "2                      Tech (0.08), Career & Business (0.01)   \n",
       "3          Career & Business (0.28), Education & Learning...   \n",
       "4          New Age & Spirituality (0.45), Paranormal (0.4...   \n",
       "5          Photography (0.60), Parents & Family (0.60), C...   \n",
       "\n",
       "                                           Top 3 Category F1  % Unique Nodes  \\\n",
       "Community                                                                      \n",
       "1              LGBT (0.64), Photography (0.15), Music (0.05)           53.33   \n",
       "2                      Tech (0.14), Career & Business (0.02)           60.71   \n",
       "3          Career & Business (0.37), Tech (0.14), Educati...           65.38   \n",
       "4          Health & Wellbeing (0.40), Support (0.40), New...           87.84   \n",
       "5          Cars & Motorcycles (0.25), Outdoors & Adventur...           83.33   \n",
       "\n",
       "           Mean Node Overlap  Median Node Overlap  \n",
       "Community                                          \n",
       "1                       1.70                    1  \n",
       "2                       1.43                    1  \n",
       "3                       1.35                    1  \n",
       "4                       1.14                    1  \n",
       "5                       1.19                    1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comm = df_comm.join(df_overlap)\n",
    "df_comm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Node-Community Centrality Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_weighted_edgelist( in_path ):\n",
    "    g = nx.Graph()\n",
    "    fin = open(in_path,\"r\")\n",
    "    for line in fin.readlines():\n",
    "        parts = line.strip().split(\" \")\n",
    "        node1, node2 = int(parts[0]), int(parts[1])\n",
    "        if node1 != node2:\n",
    "            g.add_edge( node1, node2, weight=float(parts[2]) )\n",
    "    fin.close()\n",
    "    return g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network has 1481 nodes, 708163 edges\n"
     ]
    }
   ],
   "source": [
    "g = read_weighted_edgelist(network_path)\n",
    "print( \"Network has %d nodes, %d edges\" % ( g.number_of_nodes(), g.number_of_edges() ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the weighted degree centrality for the subgraph induced by each community."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_node_community_centrality(g, comm):\n",
    "    sg = g.subgraph( comm )\n",
    "    rows = []    \n",
    "    centrality_scores = dict(nx.degree(sg, weight=\"weight\"))\n",
    "    for node in centrality_scores:\n",
    "        rows.append( { \"Id\" : node, \"Weighted Degree\" : centrality_scores[node], \n",
    "                    \"Meetup Name\" : df_meta[\"name\"].loc[node] } )\n",
    "    return pd.DataFrame(rows).set_index(\"Id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get most central nodes for each community:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_dfs = {}\n",
    "community_top_nodes = {}\n",
    "for i, comm in enumerate(communities):\n",
    "    df_c = calc_node_community_centrality( g, comm )\n",
    "    df_c = df_c.sort_values(\"Weighted Degree\", ascending=False)\n",
    "    values = []\n",
    "    for name in df_c[\"Meetup Name\"].head( min(top_nodes,len(comm)) ):\n",
    "        values.append( name.replace(\",\",\" \") )\n",
    "    community_top_nodes[i+1] = \", \".join( values )\n",
    "    cc_dfs[i+1] = df_c\n",
    "df_comm[\"Top %d Nodes\" % top_nodes ] = pd.Series(community_top_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Community</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>978</td>\n",
       "      <td>559</td>\n",
       "      <td>lgbt, gay, photography, social, food, rock, ac...</td>\n",
       "      <td>gay, lgbt, geeks, chat, social, different, dri...</td>\n",
       "      <td>LGBT (0.47), Photography (0.10), Fitness (0.07)</td>\n",
       "      <td>LGBT (1.00), Photography (0.30), Book Clubs (0...</td>\n",
       "      <td>LGBT (0.64), Photography (0.15), Music (0.05)</td>\n",
       "      <td>53.33</td>\n",
       "      <td>1.70</td>\n",
       "      <td>1</td>\n",
       "      <td>Dublin LGBT Social Meetup, GayDublin, D08 LGBT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>327</td>\n",
       "      <td>224</td>\n",
       "      <td>blockchain, crypto, tallaght, development, tec...</td>\n",
       "      <td>blockchain, crypto, bitcoin, ethereum, technol...</td>\n",
       "      <td>Tech (0.93), Career &amp; Business (0.07)</td>\n",
       "      <td>Tech (0.08), Career &amp; Business (0.01)</td>\n",
       "      <td>Tech (0.14), Career &amp; Business (0.02)</td>\n",
       "      <td>60.71</td>\n",
       "      <td>1.43</td>\n",
       "      <td>1</td>\n",
       "      <td>Crypto Ireland, Ethereum Ireland, Dublin Ether...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>890</td>\n",
       "      <td>452</td>\n",
       "      <td>startup, entrepreneurs, digital, lean, busines...</td>\n",
       "      <td>business, entrepreneurs, marketing, startup, n...</td>\n",
       "      <td>Career &amp; Business (0.54), Tech (0.37), Educati...</td>\n",
       "      <td>Career &amp; Business (0.28), Education &amp; Learning...</td>\n",
       "      <td>Career &amp; Business (0.37), Tech (0.14), Educati...</td>\n",
       "      <td>65.38</td>\n",
       "      <td>1.35</td>\n",
       "      <td>1</td>\n",
       "      <td>Startup Grind Dublin, Dublin Startup Founder 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148</td>\n",
       "      <td>632</td>\n",
       "      <td>331</td>\n",
       "      <td>meditation, yoga, healing, spiritual, heart, s...</td>\n",
       "      <td>healing, life, meditation, experience, self, e...</td>\n",
       "      <td>Health &amp; Wellbeing (0.51), Support (0.51), New...</td>\n",
       "      <td>New Age &amp; Spirituality (0.45), Paranormal (0.4...</td>\n",
       "      <td>Health &amp; Wellbeing (0.40), Support (0.40), New...</td>\n",
       "      <td>87.84</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1</td>\n",
       "      <td>Dublin Meditation Meetup, Dublin Self-Empowerm...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>216</td>\n",
       "      <td>2079</td>\n",
       "      <td>1067</td>\n",
       "      <td>hiking, international, wicklow, friends, yoga,...</td>\n",
       "      <td>fun, members, friends, time, hikes, free, soci...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.16), Outdoors &amp; Adventur...</td>\n",
       "      <td>Photography (0.60), Parents &amp; Family (0.60), C...</td>\n",
       "      <td>Cars &amp; Motorcycles (0.25), Outdoors &amp; Adventur...</td>\n",
       "      <td>83.33</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1</td>\n",
       "      <td>Kick Ass Adventures, Awesome Daytrips, Live Mu...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Size  Mean Members  Median Members  \\\n",
       "Community                                       \n",
       "1            30           978             559   \n",
       "2            28           327             224   \n",
       "3            78           890             452   \n",
       "4           148           632             331   \n",
       "5           216          2079            1067   \n",
       "\n",
       "                                           Top 10 Name Terms  \\\n",
       "Community                                                      \n",
       "1          lgbt, gay, photography, social, food, rock, ac...   \n",
       "2          blockchain, crypto, tallaght, development, tec...   \n",
       "3          startup, entrepreneurs, digital, lean, busines...   \n",
       "4          meditation, yoga, healing, spiritual, heart, s...   \n",
       "5          hiking, international, wicklow, friends, yoga,...   \n",
       "\n",
       "                                    Top 10 Description Terms  \\\n",
       "Community                                                      \n",
       "1          gay, lgbt, geeks, chat, social, different, dri...   \n",
       "2          blockchain, crypto, bitcoin, ethereum, technol...   \n",
       "3          business, entrepreneurs, marketing, startup, n...   \n",
       "4          healing, life, meditation, experience, self, e...   \n",
       "5          fun, members, friends, time, hikes, free, soci...   \n",
       "\n",
       "                                    Top 3 Category Precision  \\\n",
       "Community                                                      \n",
       "1            LGBT (0.47), Photography (0.10), Fitness (0.07)   \n",
       "2                      Tech (0.93), Career & Business (0.07)   \n",
       "3          Career & Business (0.54), Tech (0.37), Educati...   \n",
       "4          Health & Wellbeing (0.51), Support (0.51), New...   \n",
       "5          Cars & Motorcycles (0.16), Outdoors & Adventur...   \n",
       "\n",
       "                                       Top 3 Category Recall  \\\n",
       "Community                                                      \n",
       "1          LGBT (1.00), Photography (0.30), Book Clubs (0...   \n",
       "2                      Tech (0.08), Career & Business (0.01)   \n",
       "3          Career & Business (0.28), Education & Learning...   \n",
       "4          New Age & Spirituality (0.45), Paranormal (0.4...   \n",
       "5          Photography (0.60), Parents & Family (0.60), C...   \n",
       "\n",
       "                                           Top 3 Category F1  % Unique Nodes  \\\n",
       "Community                                                                      \n",
       "1              LGBT (0.64), Photography (0.15), Music (0.05)           53.33   \n",
       "2                      Tech (0.14), Career & Business (0.02)           60.71   \n",
       "3          Career & Business (0.37), Tech (0.14), Educati...           65.38   \n",
       "4          Health & Wellbeing (0.40), Support (0.40), New...           87.84   \n",
       "5          Cars & Motorcycles (0.25), Outdoors & Adventur...           83.33   \n",
       "\n",
       "           Mean Node Overlap  Median Node Overlap  \\\n",
       "Community                                           \n",
       "1                       1.70                    1   \n",
       "2                       1.43                    1   \n",
       "3                       1.35                    1   \n",
       "4                       1.14                    1   \n",
       "5                       1.19                    1   \n",
       "\n",
       "                                                 Top 5 Nodes  \n",
       "Community                                                     \n",
       "1          Dublin LGBT Social Meetup, GayDublin, D08 LGBT...  \n",
       "2          Crypto Ireland, Ethereum Ireland, Dublin Ether...  \n",
       "3          Startup Grind Dublin, Dublin Startup Founder 1...  \n",
       "4          Dublin Meditation Meetup, Dublin Self-Empowerm...  \n",
       "5          Kick Ass Adventures, Awesome Daytrips, Live Mu...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export everything to Excel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing results/meetup-analysis-overall.xlsx\n"
     ]
    }
   ],
   "source": [
    "print(\"Writing %s\" % out_path)\n",
    "writer = pd.ExcelWriter(out_path)\n",
    "df_meta.to_excel(writer, \"Nodes\")\n",
    "df_comm.to_excel(writer, \"Communities\")\n",
    "df_assign.to_excel(writer, \"Assignments\")\n",
    "# add the community memberships\n",
    "for comm_id in range( 1, len(communities)+1 ):\n",
    "    cc_dfs[comm_id].to_excel(writer, \"Community %02d\" % comm_id )\n",
    "writer.save()"
   ]
  }
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