{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ca947037",
   "metadata": {},
   "source": [
    "# Industry Community Detection\n",
    "\n",
    "_Realize that everything connects to everything else_ - Leonardo DaVinci\n",
    "\n",
    "Traditional industry classification systems, such as SIC and NAICS, group firms based on production processes or product similarities. Natural language processing techniques can be leveraged to analyze product descriptions and capture dynamic changes in industry structures over time, as proposed by Hoberg and Phillips (2016). Industry communities can be detected through network analysis, where firms are modeled as nodes in a graph, and connections between them are determined by similarities in their product and market descriptions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae4b5be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By: Terence Lim, 2020-2025 (terence-lim.github.io)\n",
    "import zipfile\n",
    "import io\n",
    "from itertools import chain\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "from finds.database import SQL\n",
    "from finds.readers import requests_get, Sectoring\n",
    "from finds.structured import BusDay, PSTAT\n",
    "from finds.recipes import graph_info\n",
    "from secret import credentials\n",
    "# %matplotlib qt\n",
    "VERBOSE = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5594dc59",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "sql = SQL(**credentials['sql'], verbose=VERBOSE)\n",
    "bd = BusDay(sql)\n",
    "pstat = PSTAT(sql, bd, verbose=VERBOSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b69912c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Industry taxonomy\n",
    "\n",
    "Industry classification, or industry taxonomy, organizes companies into groups based on shared characteristics such as production processes, product offerings, or financial market behaviors. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8a6eeab",
   "metadata": {},
   "source": [
    "### Text-based industry classification\n",
    "\n",
    "Hoberg and Phillips (2016) developed a text-based measure of firm similarity by analyzing product descriptions in 10-K filings. They construct firm-by-firm similarity scores using word vectors, filtering out common words and focusing on nouns and proper nouns, while excluding geographic terms. The similarity between firms is quantified using cosine similarity, creating a pairwise similarity matrix across firms and years.\n",
    "\n",
    "Since Item 101 of Regulation S-K mandates that firms accurately describe their key products in their 10-K filings, the TNIC scheme, based on textual similarity, provides a dynamic classification system that evolves with market changes. This method offers a more flexible alternative to traditional classification systems, capturing shifts in product markets over time.\n",
    "\n",
    "Source: [Hoberg and Phillips Industry Classification](https://hobergphillips.tuck.dartmouth.edu/industryclass.htm)\n",
    "\n",
    "The TNIC pair-wise firm similiarities are retrieved from the Hoberg and Phillips website:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c59bc4b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2023</td>\n",
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       "      <td>0.0191</td>\n",
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       "<p>666050 rows × 4 columns</p>\n",
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       "          year  gvkey1  gvkey2   score\n",
       "26307358  2023    1004    1823  0.0127\n",
       "26307359  2023    1004    4091  0.0087\n",
       "26307360  2023    1004    5567  0.0063\n",
       "26307361  2023    1004    9698  0.0075\n",
       "26307362  2023    1004   10519  0.0191\n",
       "...        ...     ...     ...     ...\n",
       "26973403  2023  351038  329141  0.0684\n",
       "26973404  2023  351038  331856  0.0769\n",
       "26973405  2023  351038  332115  0.1036\n",
       "26973406  2023  351038  347007  0.0731\n",
       "26973407  2023  351038  349972  0.0871\n",
       "\n",
       "[666050 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve TNIC scheme from Hoberg and Phillips website\n",
    "tnic_scheme = 'tnic3'\n",
    "root = 'https://hobergphillips.tuck.dartmouth.edu/idata/'   \n",
    "source = root + tnic_scheme + '_data.zip'\n",
    "if source.startswith('http'):\n",
    "    response = requests_get(source)\n",
    "    source = io.BytesIO(response.content)\n",
    "\n",
    "# extract the csv file from zip archive\n",
    "with zipfile.ZipFile(source).open(tnic_scheme + \"_data.txt\") as f:\n",
    "    tnic_data = pd.read_csv(f, sep='\\s+')\n",
    "\n",
    "# extract latest year of tnic as data frame\n",
    "year = max(tnic_data['year']) # [1989, 1999, 2009, 2019]\n",
    "tnic = tnic_data[tnic_data['year'] == year].dropna()\n",
    "tnic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34d451af",
   "metadata": {},
   "source": [
    "### Industry classification\n",
    "\n",
    "Industry classification systems such as SIC and NAICS follow hierarchical structures to categorize firms based on their economic activities:\n",
    "\n",
    "- **Standard Industrial Classification (SIC)**: Uses a 2-digit, 3-digit, and 4-digit hierarchy to classify industries.\n",
    "- **North American Industry Classification System (NAICS)**: Expands classification granularity from 2-digit to 6-digit levels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c79af05",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpermno</th>\n",
       "      <th>sic</th>\n",
       "      <th>naics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Non-missing</th>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "      <td>3827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "             lpermno   sic  naics\n",
       "Non-missing     3829  3829   3827"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# populate dataframe of nodes with gvkey (as index), permno, sic and naics\n",
    "nodes = DataFrame(index=sorted(set(tnic['gvkey1']).union(tnic['gvkey2'])))\\\n",
    "        .rename_axis(index='gvkey')\n",
    "for code in ['lpermno', 'sic', 'naics']:\n",
    "    lookup = pstat.build_lookup('gvkey', code, fillna=0)\n",
    "    nodes[code] = lookup(nodes.index)\n",
    "Series(np.sum(nodes > 0, axis=0)).rename('Non-missing').to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92da5732",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lpermno</th>\n",
       "      <th>sic</th>\n",
       "      <th>naics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Non-missing</th>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "      <td>3829</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             lpermno   sic  naics\n",
       "Non-missing     3829  3829   3829"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# supplement naics and sic with crosswalks in Sectoring class\n",
    "naics = Sectoring(sql, 'naics', fillna=0)   # supplement from crosswalk\n",
    "sic = Sectoring(sql, 'sic', fillna=0)\n",
    "nodes['naics'] = nodes['naics'].where(nodes['naics'] > 0, naics[nodes['sic']])\n",
    "nodes['sic'] = nodes['sic'].where(nodes['sic'] > 0, sic[nodes['naics']])\n",
    "Series(np.sum(nodes > 0, axis=0)).rename('Non-missing').to_frame().T "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ed00de",
   "metadata": {},
   "source": [
    "### Sector groups\n",
    "\n",
    "Industry taxonomies group detailed classifications into broader sectors for economic analysis:\n",
    "\n",
    "- Fama and French aggregate 4-digit SIC codes into industry groups consisting of 5, 10, 12, 17, 30, 38, 48, or 49 sectors.\n",
    "- The Bureau of Economic Analysis (BEA) consolidates 6-digit NAICS codes into summary-level industry groups, with updates in 1947, 1963, and 1997.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "91c0143e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3845 codes5\n",
      "3845 codes10\n",
      "3845 codes12\n",
      "3845 codes17\n",
      "3845 codes30\n",
      "3845 codes38\n",
      "3845 codes48\n",
      "3845 codes49\n",
      "3829 sic2\n",
      "3829 sic3\n",
      "3561 bea1947\n",
      "3561 bea1963\n",
      "3561 bea1997\n"
     ]
    },
    {
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>gvkey</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1004</th>\n",
       "      <td>54594</td>\n",
       "      <td>5080</td>\n",
       "      <td>423860</td>\n",
       "      <td>Cnsmr</td>\n",
       "      <td>Shops</td>\n",
       "      <td>Shops</td>\n",
       "      <td>Machn</td>\n",
       "      <td>Whlsl</td>\n",
       "      <td>Whlsl</td>\n",
       "      <td>Whlsl</td>\n",
       "      <td>Whlsl</td>\n",
       "      <td>50</td>\n",
       "      <td>508</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>21020</td>\n",
       "      <td>4512</td>\n",
       "      <td>481111</td>\n",
       "      <td>Other</td>\n",
       "      <td>Durbl</td>\n",
       "      <td>Durbl</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Trans</td>\n",
       "      <td>Trans</td>\n",
       "      <td>45</td>\n",
       "      <td>451</td>\n",
       "      <td>48</td>\n",
       "      <td>481</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>11499</td>\n",
       "      <td>3564</td>\n",
       "      <td>333413</td>\n",
       "      <td>Manuf</td>\n",
       "      <td>Manuf</td>\n",
       "      <td>Manuf</td>\n",
       "      <td>Machn</td>\n",
       "      <td>FabPr</td>\n",
       "      <td>Machn</td>\n",
       "      <td>Mach</td>\n",
       "      <td>Mach</td>\n",
       "      <td>35</td>\n",
       "      <td>356</td>\n",
       "      <td>333</td>\n",
       "      <td>333</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>10517</td>\n",
       "      <td>6141</td>\n",
       "      <td>522220</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>Money</td>\n",
       "      <td>Finan</td>\n",
       "      <td>Fin</td>\n",
       "      <td>Money</td>\n",
       "      <td>Banks</td>\n",
       "      <td>Banks</td>\n",
       "      <td>61</td>\n",
       "      <td>614</td>\n",
       "      <td>52</td>\n",
       "      <td>521CI</td>\n",
       "      <td>521CI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>20482</td>\n",
       "      <td>3845</td>\n",
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       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Instr</td>\n",
       "      <td>MedEq</td>\n",
       "      <td>MedEq</td>\n",
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       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345980</th>\n",
       "      <td>20333</td>\n",
       "      <td>5961</td>\n",
       "      <td>455110</td>\n",
       "      <td>Cnsmr</td>\n",
       "      <td>Shops</td>\n",
       "      <td>Shops</td>\n",
       "      <td>Rtail</td>\n",
       "      <td>Rtail</td>\n",
       "      <td>Rtail</td>\n",
       "      <td>Rtail</td>\n",
       "      <td>Rtail</td>\n",
       "      <td>59</td>\n",
       "      <td>596</td>\n",
       "      <td>44RT</td>\n",
       "      <td>44RT</td>\n",
       "      <td>4A0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347007</th>\n",
       "      <td>15533</td>\n",
       "      <td>2836</td>\n",
       "      <td>325414</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Chems</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>28</td>\n",
       "      <td>283</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349337</th>\n",
       "      <td>20867</td>\n",
       "      <td>3845</td>\n",
       "      <td>334510</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Instr</td>\n",
       "      <td>MedEq</td>\n",
       "      <td>MedEq</td>\n",
       "      <td>38</td>\n",
       "      <td>384</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349972</th>\n",
       "      <td>15642</td>\n",
       "      <td>2836</td>\n",
       "      <td>325414</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Other</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Chems</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>28</td>\n",
       "      <td>283</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351038</th>\n",
       "      <td>16161</td>\n",
       "      <td>2834</td>\n",
       "      <td>325412</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Cnsum</td>\n",
       "      <td>Hlth</td>\n",
       "      <td>Chems</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>Drugs</td>\n",
       "      <td>28</td>\n",
       "      <td>283</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3561 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        lpermno   sic   naics codes5 codes10 codes12 codes17 codes30 codes38  \\\n",
       "gvkey                                                                          \n",
       "1004      54594  5080  423860  Cnsmr   Shops   Shops   Machn   Whlsl   Whlsl   \n",
       "1045      21020  4512  481111  Other   Durbl   Durbl   Trans   Trans   Trans   \n",
       "1050      11499  3564  333413  Manuf   Manuf   Manuf   Machn   FabPr   Machn   \n",
       "1076      10517  6141  522220  Other   Other   Money   Finan     Fin   Money   \n",
       "1078      20482  3845  334510   Hlth    Hlth    Hlth   Other    Hlth   Instr   \n",
       "...         ...   ...     ...    ...     ...     ...     ...     ...     ...   \n",
       "345980    20333  5961  455110  Cnsmr   Shops   Shops   Rtail   Rtail   Rtail   \n",
       "347007    15533  2836  325414   Hlth    Hlth    Hlth   Other    Hlth   Chems   \n",
       "349337    20867  3845  334510   Hlth    Hlth    Hlth   Other    Hlth   Instr   \n",
       "349972    15642  2836  325414   Hlth    Hlth    Hlth   Other    Hlth   Chems   \n",
       "351038    16161  2834  325412   Hlth    Hlth    Hlth   Cnsum    Hlth   Chems   \n",
       "\n",
       "       codes48 codes49  sic2  sic3 bea1947 bea1963 bea1997  \n",
       "gvkey                                                       \n",
       "1004     Whlsl   Whlsl    50   508      42      42      42  \n",
       "1045     Trans   Trans    45   451      48     481     481  \n",
       "1050      Mach    Mach    35   356     333     333     333  \n",
       "1076     Banks   Banks    61   614      52   521CI   521CI  \n",
       "1078     MedEq   MedEq    38   384     334     334     334  \n",
       "...        ...     ...   ...   ...     ...     ...     ...  \n",
       "345980   Rtail   Rtail    59   596    44RT    44RT     4A0  \n",
       "347007   Drugs   Drugs    28   283     325     325     325  \n",
       "349337   MedEq   MedEq    38   384     334     334     334  \n",
       "349972   Drugs   Drugs    28   283     325     325     325  \n",
       "351038   Drugs   Drugs    28   283     325     325     325  \n",
       "\n",
       "[3561 rows x 16 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# include sectoring schemes\n",
    "codes = {'sic': ([f\"codes{c}\" for c in [5, 10, 12, 17, 30, 38, 48, 49]]\n",
    "                 + ['sic2', 'sic3']),\n",
    "         'naics': ['bea1947', 'bea1963', 'bea1997']}\n",
    "sectorings = {}   # store Sectoring objects\n",
    "for key, schemes in codes.items():\n",
    "    for scheme in schemes:\n",
    "        if scheme not in sectorings:\n",
    "\n",
    "            # missing value is integer 0 sic2 and sic3 shemes, else string ''\n",
    "            fillna = 0 if scheme.startswith('sic') else ''\n",
    "\n",
    "            # load the sectoring class from SQL\n",
    "            sectorings[scheme] = Sectoring(sql, scheme, fillna=fillna)\n",
    "\n",
    "            # apply the sectoring scheme to partition the nodes\n",
    "            nodes[scheme] = sectorings[scheme][nodes[key]]\n",
    "\n",
    "        # keep nodes with non-missing data\n",
    "        nodes = nodes[nodes[scheme].ne(sectorings[scheme].fillna)]\n",
    "        print(len(nodes), scheme)\n",
    "nodes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20ededd9",
   "metadata": {},
   "source": [
    "## Community structure\n",
    "\n",
    "In network analysis, **community structure** refers to the clustering of nodes (firms) into partitions (groups) based on connectivity patterns. Identifying these communities helps reveal hidden industry relationships and competitive dynamics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ccfb41d5",
   "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>2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>transitivity</th>\n",
       "      <td>0.877035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>average_clustering</th>\n",
       "      <td>0.575643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>connected</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>connected_components</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>size_largest_component</th>\n",
       "      <td>3523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>directed</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>negatively_weighted</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>edges</th>\n",
       "      <td>320352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nodes</th>\n",
       "      <td>3541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>selfloops</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>density</th>\n",
       "      <td>0.051113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            2023\n",
       "transitivity            0.877035\n",
       "average_clustering      0.575643\n",
       "connected                  False\n",
       "connected_components           9\n",
       "size_largest_component      3523\n",
       "directed                   False\n",
       "weighted                    True\n",
       "negatively_weighted        False\n",
       "edges                     320352\n",
       "nodes                       3541\n",
       "selfloops                      0\n",
       "density                 0.051113"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# populate undirected graph with tnic edges\n",
    "edges = tnic[tnic['gvkey1'].isin(nodes.index) & tnic['gvkey2'].isin(nodes.index)]\n",
    "edges = list(\n",
    "    edges[['gvkey1', 'gvkey2', 'score']].itertuples(index=False, name=None))\n",
    "G = nx.Graph()\n",
    "G.add_weighted_edges_from(edges)\n",
    "G.remove_edges_from(nx.selfloop_edges(G))  # remove self-loops: not necessary\n",
    "Series(graph_info(G, fast=True)).rename(year).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49aa2dc",
   "metadata": {},
   "source": [
    "### Measuring partitions\n",
    "\n",
    "The quality of graph partitions can be evaluated using modularity, a measure that assesses the strength of community structures by comparing observed connections within clusters to a random network model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2bf97917",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# evaluate modularity of sectoring schemes on TNIC graph\n",
    "def community_quality(G, communities):\n",
    "    \"\"\"helper to measure quality of partitions\"\"\"\n",
    "    out = {'communities': len(communities)}\n",
    "    out['modularity'] = nx.community.modularity(G, communities)\n",
    "    (out['coverage'],\n",
    "     out['performance']) = nx.community.partition_quality(G, communities)    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eeaba37f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Quality of sectoring schemes on TNIC graph (2023)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>communities</th>\n",
       "      <th>modularity</th>\n",
       "      <th>coverage</th>\n",
       "      <th>performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bea1947</th>\n",
       "      <td>40</td>\n",
       "      <td>0.330481</td>\n",
       "      <td>0.779187</td>\n",
       "      <td>0.925859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bea1963</th>\n",
       "      <td>58</td>\n",
       "      <td>0.324246</td>\n",
       "      <td>0.734745</td>\n",
       "      <td>0.948296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bea1997</th>\n",
       "      <td>61</td>\n",
       "      <td>0.324169</td>\n",
       "      <td>0.734514</td>\n",
       "      <td>0.948689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes10</th>\n",
       "      <td>10</td>\n",
       "      <td>0.335843</td>\n",
       "      <td>0.940503</td>\n",
       "      <td>0.850069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes12</th>\n",
       "      <td>12</td>\n",
       "      <td>0.336655</td>\n",
       "      <td>0.938187</td>\n",
       "      <td>0.878268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.285847</td>\n",
       "      <td>0.766719</td>\n",
       "      <td>0.794675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes30</th>\n",
       "      <td>30</td>\n",
       "      <td>0.335544</td>\n",
       "      <td>0.934385</td>\n",
       "      <td>0.899115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes38</th>\n",
       "      <td>36</td>\n",
       "      <td>0.333800</td>\n",
       "      <td>0.793237</td>\n",
       "      <td>0.890785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.331168</td>\n",
       "      <td>0.752610</td>\n",
       "      <td>0.944559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes49</th>\n",
       "      <td>49</td>\n",
       "      <td>0.331003</td>\n",
       "      <td>0.751526</td>\n",
       "      <td>0.951096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>codes5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.337074</td>\n",
       "      <td>0.945045</td>\n",
       "      <td>0.818984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sic2</th>\n",
       "      <td>67</td>\n",
       "      <td>0.327541</td>\n",
       "      <td>0.743694</td>\n",
       "      <td>0.942476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sic3</th>\n",
       "      <td>226</td>\n",
       "      <td>0.288389</td>\n",
       "      <td>0.690952</td>\n",
       "      <td>0.958297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         communities  modularity  coverage  performance\n",
       "bea1947           40    0.330481  0.779187     0.925859\n",
       "bea1963           58    0.324246  0.734745     0.948296\n",
       "bea1997           61    0.324169  0.734514     0.948689\n",
       "codes10           10    0.335843  0.940503     0.850069\n",
       "codes12           12    0.336655  0.938187     0.878268\n",
       "codes17           17    0.285847  0.766719     0.794675\n",
       "codes30           30    0.335544  0.934385     0.899115\n",
       "codes38           36    0.333800  0.793237     0.890785\n",
       "codes48           48    0.331168  0.752610     0.944559\n",
       "codes49           49    0.331003  0.751526     0.951096\n",
       "codes5             5    0.337074  0.945045     0.818984\n",
       "sic2              67    0.327541  0.743694     0.942476\n",
       "sic3             226    0.288389  0.690952     0.958297"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modularity = {}   # to collect measurements of each scheme\n",
    "for scheme in sorted(chain(*codes.values())):\n",
    "    communities = nodes.loc[list(G.nodes), scheme]\\\n",
    "                       .reset_index()\\\n",
    "                       .groupby(scheme)['gvkey']\\\n",
    "                       .apply(list)\\\n",
    "                       .to_list()    # list of lists of node labels\n",
    "    modularity[scheme] = community_quality(G, communities)\n",
    "df = DataFrame.from_dict(modularity, orient='index').sort_index()\n",
    "print(f\"Quality of sectoring schemes on TNIC graph ({year})\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36733f6b",
   "metadata": {},
   "source": [
    "### Detecting partitions\n",
    "\n",
    "\n",
    "Community detection in graphs can be performed using various algorithms, including:\n",
    "\n",
    "- **Label Propagation Algorithm**: This method assigns an initial label to each node and iteratively updates labels based on the majority of its neighbors’ labels, allowing communities to form dynamically. It is fast and works well for large networks but may produce different results on different runs due to randomness.  \n",
    "\n",
    "- **Louvain Method**: This hierarchical clustering algorithm optimizes modularity by iteratively merging small communities into larger ones, maximizing intra-community connections while minimizing inter-community edges. \n",
    "\n",
    "- **Greedy Algorithm**: This algorithm builds communities by iteratively merging pairs of nodes or groups that result in the largest modularity gain, prioritizing locally optimal choices.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "568616be",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# Run community detection algorithms\n",
    "def community_detection(G):\n",
    "    \"\"\"Helper to run community detection algorithms on an undirected graph\"\"\"\n",
    "    out = {}\n",
    "    out['label'] = nx.community.label_propagation_communities(G)\n",
    "    out['louvain'] = nx.community.louvain_communities(G, resolution=1)\n",
    "    out['greedy'] = nx.community.greedy_modularity_communities(G, resolution=1)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e3d6d43b",
   "metadata": {},
   "outputs": [],
   "source": [
    "communities = community_detection(G)\n",
    "quality = {}\n",
    "for key, community in communities.items():\n",
    "    quality[key] = community_quality(G, community)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3a336afc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modularity of community detection algorithms on TNIC graph (2023)\n"
     ]
    },
    {
     "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>communities</th>\n",
       "      <th>modularity</th>\n",
       "      <th>coverage</th>\n",
       "      <th>performance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>greedy</th>\n",
       "      <td>51</td>\n",
       "      <td>0.323848</td>\n",
       "      <td>0.989711</td>\n",
       "      <td>0.689093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>101</td>\n",
       "      <td>0.347795</td>\n",
       "      <td>0.990485</td>\n",
       "      <td>0.909486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>louvain</th>\n",
       "      <td>19</td>\n",
       "      <td>0.354818</td>\n",
       "      <td>0.824855</td>\n",
       "      <td>0.838868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         communities  modularity  coverage  performance\n",
       "greedy            51    0.323848  0.989711     0.689093\n",
       "label            101    0.347795  0.990485     0.909486\n",
       "louvain           19    0.354818  0.824855     0.838868"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame.from_dict(quality, orient='index').sort_index()\n",
    "print(f\"Modularity of community detection algorithms on TNIC graph ({year})\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bcd03885",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize Fama-French 49-industries in the detected communities\n",
    "key = 'codes49'\n",
    "for ifig, detection in enumerate(communities.keys()):\n",
    "\n",
    "    # count industries represented in each partition\n",
    "    industry = []\n",
    "    communities_sequence = sorted(communities[detection], key=len, reverse=True)    \n",
    "    for i, community in enumerate(communities_sequence):\n",
    "        industry.append(nodes[key][list(community)].value_counts().rename(i+1))\n",
    "    names = sectorings[key].sectors['name'].drop_duplicates(keep='first')\n",
    "    df = pd.concat(industry, axis=1)\\\n",
    "           .dropna(axis=0, how='all')\\\n",
    "           .fillna(0)\\\n",
    "           .astype(int)\\\n",
    "           .reindex(names)\n",
    "\n",
    "    # display as heatmap\n",
    "    fig, ax = plt.subplots(num=ifig+1, clear=True, figsize=(6, 8))\n",
    "    sns.heatmap(df.iloc[:,:10],\n",
    "                square=False,\n",
    "                linewidth=.5,\n",
    "                ax=ax,\n",
    "                yticklabels=1,\n",
    "                cmap=\"YlGnBu\",\n",
    "                robust=True)\n",
    "    if scheme.startswith('bea'):\n",
    "        ax.set_yticklabels(Sectoring._bea_industry[df.index], size=10)\n",
    "    else:\n",
    "        ax.set_yticklabels(df.index, size=10)\n",
    "    ax.set_title(f'{detection.capitalize()} Community Detection {year}')\n",
    "    ax.set_xlabel(f\"Industry representation in communities\")\n",
    "    ax.set_ylabel(f\"{key} industry\")\n",
    "    fig.subplots_adjust(left=0.4)\n",
    "    plt.tight_layout(pad=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9801c6d6",
   "metadata": {},
   "source": [
    "**References**\n",
    "\n",
    "Gerard Hoberg and Gordon Phillips, 2016, Text-Based Network Industries and Endogenous Product Differentiation.Journal of Political Economy 124 (5), 1423-1465.\n",
    "\n",
    "Gerard Hoberg and Gordon Phillips, 2010, Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis. Review of Financial Studies 23 (10), 3773-3811. "
   ]
  }
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