explainer_embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6a175446",
   "metadata": {},
   "source": [
    "# Document and Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6b0747f",
   "metadata": {},
   "source": [
    "As a last part of the analysis we wanted to test if it was possible to create a meaningful vector representation of our data in a vector space. To do so we used two techniques - `node2vec` and `doc2vec` to model different aspects of the data. While they are similar to other methods we've used in the sense that they can help us understand patterns and relations in the data, they have the benefit of very easily being converted to a low dimensional space where one can calculate the similarities between units of analysis using for example the cosine or euclidean distance. The rest of this page is structured as follows: first, we compute the document embeddings and give a brief explanation of the method. Next, we do the same for node embeddings. We finish with visualization using UMAP. However, we think that the best way to interact and understand the embeddings is in [`tensorboard`](https://www.tensorflow.org/tensorboard) and we have hosted the data on the following two links below for you to play around with. We recommend testing both PCA and UMAP for dimensionality reduction.\n",
    "\n",
    "* [Document Embeddings](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/MatPiq/social-graphs-embeddings-data/main/document_config.json)\n",
    "* [Node Embeddings](https://projector.tensorflow.org/?config=https://raw.githubusercontent.com/MatPiq/social-graphs-embeddings-data/main/node_config.json)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "id": "5eaaab73",
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   "source": [
    "#Read data and import relevant modules\n",
    "from gensim.models.doc2vec import Doc2Vec\n",
    "from collections import namedtuple\n",
    "from node2vec import Node2Vec\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import umap\n",
    "import umap.plot\n",
    "from bokeh.plotting import show\n",
    "import random\n",
    "import pandas as pd\n",
    "data = pd.read_pickle('Final_df.pickle')\n",
    "edge_list = pd.read_pickle('Final_edge_list.pickle')\n",
    "node_attributes = pd.read_pickle('Final_node_attr.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5726aebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_mapping = {'political_science':'POLISCY', 'economics':'ECON',\n",
    "                'anthropology':'ANTHRO', 'psychology':'PSY', 'sociology':'SOC'}\n",
    "\n",
    "data['title_dic'] = data[['title', 'parent']].apply(lambda row: f\"{row['title']}_{name_mapping[row['parent']]}\", axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa4c7f58",
   "metadata": {},
   "source": [
    "## Document Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4139ef0d",
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   "source": [
    "Document embeddings is an extension of `word2vec` which allows us to estimate a vectorial representation of documents using shallow neural networks {cite}`le2014distributed, mikolov2013efficient`. In our case the documents are represented by the Wikipedia articles corresponding to the social science disciplines. The task of the network is to predict a word $w_t$ based on a defined amount of surrounding words $w_{t-\\Delta}$ and $w_{t+\\Delta}$ called the context, where $\\Delta$ is the symmetric size of the context window. While this task is not necessarily very interesting in itself, it encourages the hidden layer to learn a numerical representation of the words that takes context into account. By also appending an indicator variable $x$ to each Wikipedia page we simultaneously learn the representation of documents in the same latent vector space. In a paper by {citep}`rheault2020word` for example, they showed that one can extract high-quality representations of the ideology of politicians and parties using a parliamentary corpora that can also be used for a variety of downstream tasks. Borrowing {citep}`rheault2020word` figure we display the network architecture below."
   ]
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   "id": "b8368e9f",
   "metadata": {},
   "source": [
    "<img src=\"https://github.com/MatPiq/social-graphs-embeddings-data/blob/main/doc2vec.png?raw=true\" width=\"800\"> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5642bdf4",
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   "source": [
    "To run `doc2vec` we first need to create a list of documents. Each document is a `namedtuple` containing the text and the indicator variable for the article. We also set several hyperparameters, the most important being the dimensions of the hidden layer `vector_size`. Since the data is relatively small we set this to 100, common for larger corpuses being in the range of 200-300."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e17cfda9",
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   "source": [
    "#Create a list of documents and store relevant meta data\n",
    "docs = []\n",
    "doc_meta = []\n",
    "#Define a document data obj\n",
    "document_tup = namedtuple('Doc', 'words, tags')\n",
    "for row in data.iterrows():\n",
    "    #Ignore empty articles\n",
    "    if isinstance(row[1]['cleaned_text'], str):\n",
    "        docs.append(document_tup(row[1]['cleaned_text'].split(), \n",
    "                                 [row[1]['title_dic']]\n",
    "                                 ))\n",
    "        \n",
    "        doc_meta.append([row[1]['title'], row[1]['title_dic'], row[1]['parent']])\n",
    "\n",
    "doc_meta = pd.DataFrame(doc_meta, columns=['merge_key', 'article_name', 'discipline'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1be3f4b",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting to build the vocabulary based on 4302 documents...\n",
      "Starting to train the model for 5 epochs and with vector size 100...\n"
     ]
    }
   ],
   "source": [
    "def doc2vec(docs:namedtuple, vector_size, window, \n",
    "            min_count, workers, epochs):\n",
    "    \"\"\"\n",
    "    Fits the document doc2vec model on a list of namedtuples.\n",
    "    Returns the trained model.\n",
    "    \"\"\"\n",
    "    model = Doc2Vec(vector_size=vector_size, window=window, min_count=min_count, workers=workers, epochs=epochs)\n",
    "    print(f'Starting to build the vocabulary based on {len(docs)} documents...')\n",
    "    model.build_vocab(docs)\n",
    "    print(f'Starting to train the model for {epochs} epochs and with vector size {vector_size}...')\n",
    "    model.train(docs, total_examples=model.corpus_count, epochs=model.epochs)\n",
    "    return model\n",
    "\n",
    "doc2vec_fitted = doc2vec(docs, vector_size=100, window=20, min_count=10, workers=8, epochs=5)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "199cf94c",
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   "source": [
    "#Extract the document embeddings from trained model\n",
    "doc_labs = list(doc2vec_fitted.dv.key_to_index.keys())\n",
    "doc_embs = np.array([doc2vec_fitted.dv[lab] for lab in doc_labs])\n",
    "doc_df = pd.DataFrame(doc_embs, index = doc_labs)\n",
    "#Save the embeddings and labels locally as TSV\n",
    "doc_df.to_csv('document_embeddings.tsv', sep = '\\t')\n",
    "doc_meta.to_csv('document_meta.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70e78320",
   "metadata": {},
   "source": [
    "## Node Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47025864",
   "metadata": {},
   "source": [
    "`Node2vec` was introduced in {citep}`grover2016node2vec` and is in many ways just like `doc2vec` explained above with the notable difference that we are working with a graph instead of a document of text. The trick of `node2vec` is to first create a representation of the graph as a string that encodes the connection between nodes. As visualized below, this is done by taking random walks in the graph and letting the connections form artificial \"sentences''. This leads to a data structure that can be passed to the normal `word2vec` model and generates one embedding corresponding to each node."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e099b16c",
   "metadata": {},
   "source": [
    "<img src=\"https://miro.medium.com/max/1838/1*GbZk_M_HqCu8Y99J_FzhQw.gif\" width=\"800\"> "
   ]
  },
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   "cell_type": "markdown",
   "id": "aadd3526",
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   "source": [
    "Before running the model we load the edge list and create the undirected `networkx` graph object and select the giant connected component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64152b3d",
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   "source": [
    "#Create subgraph and extract giant component\n",
    "G = nx.Graph()\n",
    "G.add_edges_from(edge_list)\n",
    "gcc = sorted(nx.connected_components(G), key=len, reverse=True)\n",
    "G = G.subgraph(gcc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "72dbeef4",
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    {
     "name": "stderr",
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     "text": [
      "Computing transition probabilities: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2649/2649 [00:05<00:00, 502.60it/s]\n",
      "Generating walks (CPU: 1): 100%|██████████| 25/25 [00:23<00:00,  1.05it/s]\n",
      "Generating walks (CPU: 2): 100%|██████████| 25/25 [00:24<00:00,  1.03it/s]\n",
      "Generating walks (CPU: 4): 100%|██████████| 25/25 [00:25<00:00,  1.01s/it]\n",
      "Generating walks (CPU: 5): 100%|██████████| 25/25 [00:25<00:00,  1.00s/it]\n",
      "Generating walks (CPU: 3): 100%|██████████| 25/25 [00:33<00:00,  1.32s/it]\n",
      "Generating walks (CPU: 6): 100%|██████████| 25/25 [00:32<00:00,  1.30s/it]\n",
      "Generating walks (CPU: 7): 100%|██████████| 25/25 [00:31<00:00,  1.27s/it]\n",
      "Generating walks (CPU: 8): 100%|██████████| 25/25 [00:31<00:00,  1.25s/it]\n"
     ]
    }
   ],
   "source": [
    "#Train the model\n",
    "node2vec_fitted = Node2Vec(G, dimensions=100, walk_length=40, num_walks=200, workers=8).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a09711a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract embeddings, create meta data and save locally\n",
    "node_labs = list(node2vec_fitted.wv.key_to_index.keys())\n",
    "node_embs = [node2vec_fitted.wv[lab] for lab in node_labs]\n",
    "node_df = pd.DataFrame(node_embs, index = node_labs)\n",
    "node_meta = pd.DataFrame(node_labs, columns=['merge_key']).merge(doc_meta, how='left').dropna()\n",
    "node_df = node_df.loc[node_df.index.isin(node_meta['merge_key'])]\n",
    "#Save the embeddings and labels locally as TSV\n",
    "node_meta = node_meta[['article_name', 'discipline']]\n",
    "\n",
    "node_meta.to_csv('node_meta.tsv', sep = '\\t', index=False)\n",
    "node_df.to_csv('node_embeddings.tsv', sep='\\t', index=False, header=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae8e48f",
   "metadata": {},
   "source": [
    "## Analysis of embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54da49d",
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   "source": [
    "Below we create two plots and reduce the dimensions of the embeddings using the Uniform Manifold Approximation Projection for Dimension Reduction technique (UMAP) introduced in {cite}`mcinnes2018umap`. They show that Umap tends to be good at preserving the global structure of the embedding space while at the same time being fast.  \n",
    "\n",
    "Although one should probably be cautious when interpreting embeddings and especially their projections onto 2D-space, we generally find that both models have generated intuitive and useful representations of the data they were trained on. Moreover, the patterns to a large extent coincide with results from the previous methods we have implemented. In the plot based on node embeddings, the different disciplines are clearly clustered in groups. Looking at the y-axis, the disciplines furthest apart from each other are Economics and Anthropology, while Political Science and Sociology are mostly situated in the middle. One potential explanation for this pattern is their methodological and epistemological differences. Economics comes from a positivist tradition and mostly uses mathematics both in empirical and theoretical work while Anthropology is on the other side of the same spectrum. Political Science and Sociology on the other hand, tend to use both quantitative and qualitative methods and be grounded in both interpretivism and positivism. A result that perhaps challenges this hypothesis is the proximity of Psychology and Anthropology. In this case, their relative positions rather seem to stem from overlap in subject matter rather than the particular methodological or epistemological approach. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb6a1c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_umap(embeddings:np.array, meta:pd.DataFrame):\n",
    "    \"\"\"\n",
    "    Reduces dimensionality to two dims using UMAP \n",
    "    and creates interactive plot.\n",
    "    \"\"\"\n",
    "    mapper = umap.UMAP(n_neighbors=15, min_dist=0.01, \n",
    "                       n_components=2, metric='cosine').fit(embeddings)\n",
    "    \n",
    "    p = umap.plot.interactive(mapper, labels=meta.discipline, alpha = 1,\n",
    "                          point_size=3, theme='inferno', hover_data=meta,\n",
    "                          interactive_text_search_columns=['article_name', 'discipline'],\n",
    "                          interactive_text_search=True, \n",
    "                          interactive_text_search_alpha_contrast = 0.95)\n",
    "\n",
    "    print('Drawing 20 random article that you can search for:')\n",
    "    suggestions = random.sample(meta['article_name'].tolist(), 20)\n",
    "    print(suggestions)\n",
    "    print('-'*66)\n",
    "    print('You can also just search for a discipline')\n",
    "    umap.plot.output_notebook()\n",
    "    show(p)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "421beb24",
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    {
     "name": "stdout",
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     "text": [
      "Drawing 20 random article that you can search for:\n",
      "['experimental_economics_ECON', 'celeb_jihad_POLISCY', 'inter-municipal_cooperation_POLISCY', 'names_for_the_human_species_ANTHRO', 'religion_ANTHRO', 'far-right_politics_POLISCY', 'women-are-wonderful_effect_PSY', 'discrete_choice_ECON', 'mapping_controversies_SOC', 'dispersed_knowledge_ECON', 'manorialism_ANTHRO', 'real-time_economy_ECON', 'responsibility_to_protect_POLISCY', 'martens_centre_POLISCY', 'universe_(economics)_ECON', 'cultural_movement_ANTHRO', 'vulnerable_adult_SOC', 'sustainability_standards_and_certification_SOC', 'kansas_city_preventive_patrol_experiment_POLISCY', 'archon_fung_POLISCY']\n",
      "------------------------------------------------------------------\n",
      "You can also just search for a discipline\n"
     ]
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       "    const el = document.getElementById(\"1276\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.1.min.js\", \"https://unpkg.com/@holoviz/panel@0.12.5/dist/panel.min.js\"];\n",
       "  const css_urls = [];\n",
       "  \n",
       "\n",
       "  const inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
       "    }\n",
       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"1276\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"f8bb2c47-6a81-4d75-9b80-5bb8ba32d932\" data-root-id=\"1275\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[2649]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[2649]}},\"selected\":{\"id\":\"1308\"},\"selection_policy\":{\"id\":\"1307\"}},\"id\":\"1233\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"tooltips\":[[\"article_name\",\"@{article_name}\"],[\"discipline\",\"@{discipline}\"]]},\"id\":\"1258\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1251\",\"type\":\"PanTool\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1233\"},\"glyph\":{\"id\":\"1268\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1270\"},\"nonselection_glyph\":{\"id\":\"1269\"},\"view\":{\"id\":\"1272\"}},\"id\":\"1271\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1308\",\"type\":\"Selection\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1305\"},\"group\":null,\"major_label_policy\":{\"id\":\"1306\"},\"ticker\":{\"id\":\"1244\"},\"visible\":false},\"id\":\"1243\",\"type\":\"LinearAxis\"},{\"attributes\":{\"args\":{\"matching_alpha\":0.95,\"non_matching_alpha\":0.050000000000000044,\"search_columns\":[\"article_name\",\"discipline\"],\"source\":{\"id\":\"1233\"}},\"code\":\"\\n                    var data = source.data;\\n                    var text_search = cb_obj.value;\\n                    \\n                    var search_columns_dict = {}\\n                    for (var col in search_columns){\\n                        search_columns_dict[col] = search_columns[col]\\n                    }\\n                    \\n                    // Loop over columns and values\\n                    // If there is no match for any column for a given row, change the alpha value\\n                    var string_match = false;\\n                    for (var i = 0; i < data.x.length; i++) {\\n                        string_match = false\\n                        for (var j in search_columns_dict) {\\n                            if (String(data[search_columns_dict[j]][i]).includes(text_search) ) {\\n                                string_match = true\\n                            }\\n                        }\\n                        if (string_match){\\n                            data['alpha'][i] = matching_alpha\\n                        }else{\\n                            data['alpha'][i] = non_matching_alpha\\n                        }\\n                    }\\n                    source.change.emit();\\n                \"},\"id\":\"1274\",\"type\":\"CustomJS\"},{\"attributes\":{\"axis\":{\"id\":\"1247\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null,\"visible\":false},\"id\":\"1250\",\"type\":\"Grid\"},{\"attributes\":{\"js_property_callbacks\":{\"change:value\":[{\"id\":\"1274\"}]},\"title\":\"Search:\"},\"id\":\"1273\",\"type\":\"TextInput\"},{\"attributes\":{},\"id\":\"1307\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"color\"},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"field\":\"color\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"field\":\"color\"},\"size\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1269\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1235\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1241\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1255\",\"type\":\"ResetTool\"},{\"attributes\":{\"background_fill_color\":\"black\",\"below\":[{\"id\":\"1243\"}],\"center\":[{\"id\":\"1246\"},{\"id\":\"1250\"}],\"height\":800,\"left\":[{\"id\":\"1247\"}],\"renderers\":[{\"id\":\"1271\"}],\"title\":{\"id\":\"1299\"},\"toolbar\":{\"id\":\"1259\"},\"width\":800,\"x_range\":{\"id\":\"1235\"},\"x_scale\":{\"id\":\"1239\"},\"y_range\":{\"id\":\"1237\"},\"y_scale\":{\"id\":\"1241\"}},\"id\":\"1234\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"field\":\"color\"},\"hatch_alpha\":{\"value\":0.2},\"hatch_color\":{\"field\":\"color\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"field\":\"color\"},\"size\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1270\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1302\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1244\",\"type\":\"BasicTicker\"},{\"attributes\":{\"axis\":{\"id\":\"1243\"},\"coordinates\":null,\"group\":null,\"ticker\":null,\"visible\":false},\"id\":\"1246\",\"type\":\"Grid\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1257\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"1275\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.1\"}};\n",
       "  const render_items = [{\"docid\":\"f123701e-b040-4148-9abc-28bea40ae72a\",\"root_ids\":[\"1275\"],\"roots\":{\"1275\":\"f8bb2c47-6a81-4d75-9b80-5bb8ba32d932\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1275"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_umap(node_embs, node_meta)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d88958",
   "metadata": {},
   "source": [
    "At first glance, the document embeddings might seem completely random by the position of the points. However, when analysing the data closer we find this not to be the case. By looking at the neighbours of some Wikipedia page, i.e. the points closest in space, we find that they are often similar in content. As an example, if we look at the most similar pages to the Anthropology page [mobile_ethnography](https://en.wikipedia.org/wiki/Mobile_ethnography) which is a qualitative method that utilizes smartphones we find pages similar to digital methods both in Anthropology and in other fields. In summary, we think that one possible interpretation of these findings is that academic disciplines, as understood through Wikipedia, tend to explicitly reference their own discipline much more than to others. However, this cannot be fully explained by there being no overlap in substantive content as seen by the doc2vec results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3636a047",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drawing 20 random article that you can search for:\n",
      "['kurt_heinrich_wolff_SOC', 'noctcaelador_PSY', 'rosa_brooks_POLISCY', 'james–lange_theory_PSY', 'roy_model_ECON', 'food_competition_SOC', 'social_indicators_research_SOC', 'gish_gallop_POLISCY', 'irish_honours_system_ANTHRO', 'rosa_luxemburg_foundation_POLISCY', 'invoke_malaysia_POLISCY', \"mandeville's_paradox_ECON\", 'piréz_people_POLISCY', 'sexual_jihad_POLISCY', 'distributive_tendency_POLISCY', 'paradox_of_thrift_ECON', 'bratachari_movement_ANTHRO', 'consistent_pricing_process_ECON', 'informational_listening_SOC', 'consummation_ANTHRO']\n",
      "------------------------------------------------------------------\n",
      "You can also just search for a discipline\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1155\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
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       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
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       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
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       "    node.appendChild(script);\n",
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       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
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       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
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       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
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       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
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       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
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       "    }\n",
       "\n",
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       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "  };\n",
       "\n",
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       "    element.appendChild(document.createTextNode(css));\n",
       "    document.body.appendChild(element);\n",
       "  }\n",
       "\n",
       "  \n",
       "  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.1.min.js\", \"https://unpkg.com/@holoviz/panel@0.12.5/dist/panel.min.js\"];\n",
       "  const css_urls = [];\n",
       "  \n",
       "\n",
       "  const inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "    \n",
       "    \n",
       "    }\n",
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       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if (root.Bokeh !== undefined || force === true) {\n",
       "      \n",
       "    for (let i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i].call(root, root.Bokeh);\n",
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       "    if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      const cell = $(document.getElementById(\"1155\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
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       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"3f533367-edc4-435e-8d4e-05ba174a03a6\" data-root-id=\"1154\"></div>\n"
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     "data": {
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       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "    \n",
       "  const docs_json = {\"9eced83b-422c-4406-be9d-aa7c9b09ce9d\":{\"defs\":[{\"extends\":null,\"module\":null,\"name\":\"ReactiveHTML1\",\"overrides\":[],\"properties\":[]},{\"extends\":null,\"module\":null,\"name\":\"FlexBox1\",\"overrides\":[],\"properties\":[{\"default\":\"flex-start\",\"kind\":null,\"name\":\"align_content\"},{\"default\":\"flex-start\",\"kind\":null,\"name\":\"align_items\"},{\"default\":\"row\",\"kind\":null,\"name\":\"flex_direction\"},{\"default\":\"wrap\",\"kind\":null,\"name\":\"flex_wrap\"},{\"default\":\"flex-start\",\"kind\":null,\"name\":\"justify_content\"}]},{\"extends\":null,\"module\":null,\"name\":\"TemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]},{\"extends\":null,\"module\":null,\"name\":\"MaterialTemplateActions1\",\"overrides\":[],\"properties\":[{\"default\":0,\"kind\":null,\"name\":\"open_modal\"},{\"default\":0,\"kind\":null,\"name\":\"close_modal\"}]}],\"roots\":{\"references\":[{\"attributes\":{\"children\":[{\"id\":\"1152\"},{\"id\":\"1113\"}]},\"id\":\"1154\",\"type\":\"Column\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1173\"},\"group\":null,\"major_label_policy\":{\"id\":\"1174\"},\"ticker\":{\"id\":\"1123\"},\"visible\":false},\"id\":\"1122\",\"type\":\"LinearAxis\"},{\"attributes\":{\"args\":{\"matching_alpha\":0.95,\"non_matching_alpha\":0.050000000000000044,\"search_columns\":[\"article_name\",\"discipline\"],\"source\":{\"id\":\"1112\"}},\"code\":\"\\n                    var data = source.data;\\n                    var text_search = cb_obj.value;\\n                    \\n                    var search_columns_dict = {}\\n                    for (var col in search_columns){\\n                        search_columns_dict[col] = search_columns[col]\\n                    }\\n                    \\n                    // Loop over columns and values\\n                    // If there is no match for any column for a given row, change the alpha value\\n                    var string_match = false;\\n                    for (var i = 0; i < data.x.length; i++) {\\n                        string_match = false\\n                        for (var j in search_columns_dict) {\\n                            if (String(data[search_columns_dict[j]][i]).includes(text_search) ) {\\n                                string_match = true\\n                            }\\n                        }\\n                        if (string_match){\\n                 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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[4302]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float32\",\"order\":\"little\",\"shape\":[4302]}},\"selected\":{\"id\":\"1176\"},\"selection_policy\":{\"id\":\"1175\"}},\"id\":\"1112\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1130\",\"type\":\"PanTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"field\":\"color\"},\"hatch_alpha\":{\"value\":0.2},\"hatch_color\":{\"field\":\"color\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"field\":\"color\"},\"size\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1149\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"BasicTicker\"},{\"attributes\":{\"background_fill_color\":\"black\",\"below\":[{\"id\":\"1122\"}],\"center\":[{\"id\":\"1125\"},{\"id\":\"1129\"}],\"height\":800,\"left\":[{\"id\":\"1126\"}],\"renderers\":[{\"id\":\"1150\"}],\"title\":{\"id\":\"1167\"},\"toolbar\":{\"id\":\"1138\"},\"width\":800,\"x_range\":{\"id\":\"1114\"},\"x_scale\":{\"id\":\"1118\"},\"y_range\":{\"id\":\"1116\"},\"y_scale\":{\"id\":\"1120\"}},\"id\":\"1113\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1136\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1112\"},\"glyph\":{\"id\":\"1147\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1149\"},\"nonselection_glyph\":{\"id\":\"1148\"},\"view\":{\"id\":\"1151\"}},\"id\":\"1150\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1173\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"alpha\"},\"fill_color\":{\"field\":\"color\"},\"hatch_alpha\":{\"field\":\"alpha\"},\"hatch_color\":{\"field\":\"color\"},\"line_alpha\":{\"field\":\"alpha\"},\"line_color\":{\"field\":\"color\"},\"size\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1147\",\"type\":\"Circle\"},{\"attributes\":{\"axis\":{\"id\":\"1126\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null,\"visible\":false},\"id\":\"1129\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1118\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1135\",\"type\":\"HelpTool\"},{\"attributes\":{},\"id\":\"1174\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1123\",\"type\":\"BasicTicker\"},{\"attributes\":{\"tools\":[{\"id\":\"1130\"},{\"id\":\"1131\"},{\"id\":\"1132\"},{\"id\":\"1133\"},{\"id\":\"1134\"},{\"id\":\"1135\"},{\"id\":\"1137\"}]},\"id\":\"1138\",\"type\":\"Toolbar\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"color\"},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"field\":\"color\"},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"field\":\"color\"},\"size\":{\"value\":3},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1148\",\"type\":\"Circle\"},{\"attributes\":{},\"id\":\"1133\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1175\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1114\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1170\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{},\"id\":\"1116\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1176\",\"type\":\"Selection\"},{\"attributes\":{\"coordinates\":null,\"group\":null},\"id\":\"1167\",\"type\":\"Title\"}],\"root_ids\":[\"1154\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.1\"}};\n",
       "  const render_items = [{\"docid\":\"9eced83b-422c-4406-be9d-aa7c9b09ce9d\",\"root_ids\":[\"1154\"],\"roots\":{\"1154\":\"3f533367-edc4-435e-8d4e-05ba174a03a6\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1154"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_umap(doc_embs, doc_meta[['article_name', 'discipline']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "effd7cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oD43He+PGbO5VMX4Us1U/Wsg+KNMy19Wfxm5Ltb6znPO9eLHSVL3MSR6shHd/UrsLtCrXmUWP9W70qlGOvBaU9+Hm+/cyqiQ3KzWO3DflHPtl9dTWAqrUrXr/hpk07t9tPL7KVOFtr2Pe50g91rsT+l4ve9VjeNX35kV8bui7P2EfP4oXq3LOOiV4m+Nk4H2oVHCdRl2h8XzcNrdtSB+bFT0HDPk3VkTEr1vt1OW6mlU/t+f4eYp29CvnTlPN/9V5d2aMl6F6LAAvKcFPAAAAAG6K+ovLjSmmW62/+LxW6LOsq/5ieGeGL3EfNR43v8C+qV9UreJ2jQySRXwXoGke127OedgUxbXml+qd2brVnhkDmB/XD64w9e7jFgLVCzPn8/jayv7vVE+dTxmU7MaU06Ivah+UIMeT6qlJoacXrFGYuGnp15kljfWTsm1j5ZwPYnD7OymlhUxjG/Hdvqk/P+xN2jdlu+pg3FymQo5Y/f6NMeu47yxzHQs6R+r2Px6xTET0wuyNpyZ9xh1npvfmBX5uOJpyXS+M3xmvEW2OxWbgfuK5VcZW/Rl4bH/moB6f3SnHdfP95Sqh3mk192nrn6dWzFnjhyoz3+Ka/469ooOIuJNzfjzDNeKFad3nFOwdFjDtzmE9ALASBD8BAAAAuCmaXxSOrEZTvnTtVE9d90vXgYpk01RGqpY9j8Ev7Jpfpja/TFtYAGbOVm67pgxCNPvdHHfTvGYVQ6/DPG08nqXfJyXAtU7meR63oRkomSkoOaVF7YPm396fdj3rbkWuM6s+1pvjYS7V5UZonmfTjs16uatUq5zWqvdvqCnH/UAoaNZAUMvrWMQ5Up+j3Smarpd5Y9r+NMz7vfk6nxumMqLC8dTXiDbHSQmJ1u1tTvHDs+Y5vLDPSmU76vN/2nU391nnCj8Gmlbzmqbq52I0K6U3w6XNyulb0/yYo2HY8vOoAtppPrHGPxYCgIkEPwEAAAC4EcqXT3VFpHFhlOb0mpMqqU1Shxav0tZAJZ3Gl6nNL6p2Z5hKcpWt2nZddZrN/2u1F6uleYw6M7x2HqHEeZvnedyGgTDJnKqpLmofNKchX7eQ8FWtynVmpcf6kLF9neqGs3q7un80bbClLNe/Zs6zv6vev2GmHffdFVrHKp4jdRixc8U25v3efJ3PDbNoVoGcdkzPYyw2g4qTqn7W5/BUlZBbNNM0730lNNc8tnOpHDykQnFEI4jN4pXKsPV788YV/t32QsXPmM81Yl1+YAcArRD8BAAAAOAmqSt3jqtGU39Rda3A0ZDKPsdXaKYZKvhuGsYh1YQiIvZTShcppf2U0sOU0tp9wbWC2zXsy8hplutOesGCv9RvU/eqr5thStyVMO/z+LpKtav6fGh9/y54HwyE29f4HJnV0q8zqz7WK/UYX8h7QXnP6VRPbaaUDqe9RVW5bB4V6la9f2NMO+5XYh0LPEe+qO5PE1ysz4NmZc1pLOK9uTvn9iPiuyBiva5prxGtj8WyT+tQ5MgwXJmSvlM9tehp3ps/fJtlPDQ/s8+zUn8zTPtE1c+V8G7j8e4sx2XE55R5VI5tVkRW7ROAG+37y+4AAAAAALQl53yUUqqfehyNL9/Ll661637p2vyyeeYvl3LO3Ua/m22+G70vxjrVc53ofbm8HRGRUupGxMcRsb9GobtV2q6rfil4Y75MLKGifrBoI64+lewXkxdZOYs4j6+jORXmPMbdQvbBkED3jTmHprAK15lVH+t951GF4VJKnQUEhDuNx/1r4lVsRPsB7c6QdaxS/0ZZxDm+jufIWVyGnn46rr0hP2S6ShXS1t+bW/zccBVfRBUam/IaMa+xuBvVvydSSts552E/LKunpO/OqXL3UNWx6uuklPI123w4j20o589BDIZo34v1rCZ/Y5TjshcRT8pTneiN/Vmqv57EYNhzHteMNq6XALA2VPwEAAAA4Kapv2gdVo2m/nLqrFQNuo7ml/nda7YX0Qh5lS+y78f4L6w70fuC9DSltOgKQldyU7drXaSUtlJKuymlp+XL/6fRq2x2GL0vcq9azWkdg3xzP4+vqdN4vIjgZ7eFNoftg07j8TqOl3W26mO9r1mZbx7raFr16tmr3r+bYlHnSF3VsJNSejJkmb7688/ZFX8Ic+1r7Rw/N1xFc3sWcY0YakjI84UgXKmMWO+fj+fZpyHmMTX7XKZ7L5ohz1mnFWcOcs47MXhN3B5SJXmcZgXleVT8bKNqMwCsDcFPAAAAAG6agXBgXeGzfOm6NWrZlnTm0UbO+TznfC96X4RO+vJ+O6W0Fl9y3dTtWmUppe2UUj+s8STaDxN1W25vGTor0sYydVakDearsyJtrKI7Oed0xdsiqmmuev9uis482ig/ftmrntpNKe3Wy6SUNsrnnvp9eti08dPoXvF1i/jccBPU4c/NIdWt3248XvSPmergZDd6FWdnvTU/p2/Nawr2cn7U+3RSOJrFaU75/ocZXtusENsZUtH4yobM7BGh4icAN5zgJwAAAAA3SgkzdKun6mkVm1+6tlFtp9t43EbFoZEByJzzXglK3otepZ2jIX2I6H0ZuzZfkN7U7Vo1KaXD6IUN6kBCN3r7eyciHkTEvZxziquHS9ZRt/F4rufxFXQbjzsttj1qHfPaB831CBAtVrfxeNXGel9zXDQrgM5DcztWbWyuev9uim7j8TzPkWZluicppZxSOi1By6cx+IOlxy1Uqp/JCn9uWMY1YpxmkLMZQKv3zfkiw9clWNepntrJOd+f9Ra9Kv1NzX9btWm38fi9Oa6LKeWcj6IXBO7bHBG4HPba83jxethm5djmGDkpIWIAuLEEPwEAAAC4iUZN915/6drWF0HNL6/uzdrAkCnyupNeUyplHuScH+Wc70Tvy9hmRZPmF6Yr76Zu1yooVcPq8+EsIh7knO+U/b2Xcz6pQiXdhXdyeZZyHs+gGWiZR+BrIftgSGhJeG2xVn2s9w2MiwUFN5r75qcLWOcsVr1/N8VCzpGyTD/U2QwBbsbgOXAeEfeHTCk+Vyv+uWFg/C873FWCnPVxbIbZ5j3jwDjNQO6VfvhW9nGzYuPcpnsv46peXyelZMr31XCdqp87jccPh1TInVm5pjavvf7NCMCNJ/gJAAAAwE30wnTvc5zm/YvG46tMV9d8zczTmeecz3LOD2Iw9BptTp+3DDd1uxatVOKp99lRqeBk+sOelTiPRylhi+6YdbVhkfugDscIfi7WSo/1iIjyfl2Pi4VUxivnWR36e7CI9U5r1ft3gyzqHOlXpO9W1RQP4nK8d8v9nZzzvUVWiIxY7c8NJSTWqZ5a6L4Zo/63xUY/zDakGuJCA7wxWJXzuj98+2Pj8bBp7dvUDAkK8q2Acj2qx3EnpTTVsSkVQ5sB+zb+Xd4Mn56twvUKAOZN8BMAAACAG6dUiKm/BP51DH553S1fOrWxrm68ON3drF+ANqvlXGcK+uaXbs3KJ+vqpm7XogwEhHLO00zH2plPV1bPCp7Hwwx8ed12+HnB+6C5LU9mXA9XtCZjvTl18CKDG/W6WqlC1rJV79/aW+A50m/zb2W9ZznnxyVcmUpVzfs5570Z192WVf7c0PxMuBLhrlKRtVs91Q98/rp6bqFTT5fQaad66loBu/Lvp27j6amm+b7i+lT9XF3NUO6TGa6VzdduTTtd/DDlc2Tz34bNqqQAcCMJfgIAAABwU9VfbG7F4JeubYdU3m88nroaTQlv1V+SXesL4SHTKN8IN3W7FqieEnXaylivzqMjK2xlzuMRmlW2ml+ajzPtF/GL2gfN9bw37XpoxTLG+izBuebYXuS0yM19segpmSdZ9f7dFIs4Rzrlv6sa3l3054ap9kOZzrkZEFul86D+N0b/3x71DzUWXbGy/vdPtPTDt+a/o+Y23XvRPB934uX7jLpyynXtSu/XZRw2K98elvN7JuWa2zyv9hZdJRkAlkXwEwAAAICbqv5SshODXxK3+gXxkCnrHk5TtaRMZ3vYeHqnucwslf1Km7VpA5MLDR4scLvoqffXtF+qrvs0wjON6Xmex20YUmVrqupIpTLWVPtiUfughAWuNEVotc7m+pjSksb65pDr+LB17MeLobmFXe+HVJfbmvG9amOeFWyX0L9VDSXO1YLOkW71ulWserzozw1TXSNi+HTOq/SZsH4v61eL7ZTH3UVOPV32Zz1uW5ntIF78d9TGVQJ70yoBvnq/bUTENBVombNSkbg+/6au3Jlzfhwv/nvus1nGUnn/O248fZZzbv1zOACsKsFPAAAAAG6kEiwa9gXn+ZwqgDS/gDwc98VX+SL4NAanXxxWneSnEXGcUjqe8ouwF74QH7Nst7o/NDhSQiLzmFJxntvFiwa+WC3hqpFKCGVuU3fOUbe6f5UxPa/zuC3NL7IPx4W+St9nDbovah/sxODxejJN+Cml9DCl9DR6QazOiGW2Ukq5up1OavcltIyx/tm4P5bwb/PcXEZ4490YHJvHM4Ssn8b8q9/Nu39124v+bLBK5n2O1GGl3ZTSaUppf8RtN6W0PUvItwXL+Nww8hpRfjB0HCs+nXMJodYhxfoa1qxwOG9vNx638sO3Mqabgb15X/ea7wU3/fqzTprHpvlvtnHux+BY6kTE6ZSfB/djSOgzIn45w/oBYO0JfgIAAABwkw37gnMu00GWL0GbX3oelmDjwxKS6KSUNssXVU9jsJLWqOok/WW2ovdF2GlK6UkJNvXb3CiBgKcx+KX7pEppX1T3O/0QZr8aZ9XPeQRv5rldvKg57rfL8d7qh+fKPu/v70VPRdqWa43pOZ7Hrcg5H8SLoefjlNJAALTq38xVMRe1D0o4v/nlfD/8tN24DmyV8NNF2ab++kz1ekVLGuubKaWL/vGN+G6s9q87zaDHUqZqLWNzWOhv2DVzq4TyLuLyOjvXcbmA/i3zs8HKmPc5Uq7n3eqpzeiF2YbdnkTv+B2XMPu0P5q5jmV8bhh2jehXqf1/xYtB5FWdzrned2+PeH4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      "text/plain": [
       "<Figure size 2400x1200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "def plot_most_similar(model, query, embedding_type = 'doc'):\n",
    "    with plt.style.context('science'):\n",
    "        fig, ax = plt.subplots(dpi=300, figsize=(8,4))\n",
    "        if embedding_type == 'doc':\n",
    "            res = reversed(model.dv.most_similar(query))\n",
    "        if embedding_type == 'word':\n",
    "            res = reversed(model.wv.most_similar(query))\n",
    "        for x, y in res:\n",
    "            ax.plot(y,x, \"ko\")\n",
    "            ax.set_title(f'Most similar to {embedding_type}: {query}')\n",
    "            ax.set_xlabel('Cosine Similarity')\n",
    "            ax.minorticks_off()\n",
    "\n",
    "plot_most_similar(doc2vec_fitted, query = 'mobile_ethnography_ANTHRO')        "
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "794ed8d0b773cbcd7b7ec7f3b705e2b7a8437127647cc51e7d6c4b4e7ca1b777"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}