icml2020_analysis.ipynb

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      "name": "icml2020_analysis.ipynb",
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    }
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          "height": 17
        },
        "outputId": "ba4fe153-6336-4c17-d5d9-c502e846c075"
      },
      "source": [
        "from IPython.core.display import display, HTML\n",
        "display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
        "\n",
        "from collections import defaultdict as ddict, Counter\n",
        "from itertools import chain\n",
        "\n",
        "import pandas as pd\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import plotly.io as pio"
      ],
      "execution_count": 1,
      "outputs": [
        {
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            "tags": []
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      "metadata": {
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        "colab": {
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          "height": 136
        },
        "outputId": "b81230d8-b3d9-47a0-bcd3-ba708785ebd2"
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      "source": [
        "# (only for collab)\n",
        "!git clone https://github.com/nd7141/icml2020.git\n",
        "!cp icml2020/university2.csv .\n",
        "!cp icml2020/icml_2020_papers.txt .\n",
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'icml2020'...\n",
            "remote: Enumerating objects: 11, done.\u001b[K\n",
            "remote: Counting objects:   9% (1/11)\u001b[K\rremote: Counting objects:  18% (2/11)\u001b[K\rremote: Counting objects:  27% (3/11)\u001b[K\rremote: Counting objects:  36% (4/11)\u001b[K\rremote: Counting objects:  45% (5/11)\u001b[K\rremote: Counting objects:  54% (6/11)\u001b[K\rremote: Counting objects:  63% (7/11)\u001b[K\rremote: Counting objects:  72% (8/11)\u001b[K\rremote: Counting objects:  81% (9/11)\u001b[K\rremote: Counting objects:  90% (10/11)\u001b[K\rremote: Counting objects: 100% (11/11)\u001b[K\rremote: Counting objects: 100% (11/11), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 11 (delta 2), reused 10 (delta 1), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (11/11), done.\n",
            "icml2020  icml_2020_papers.txt\tsample_data  university2.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V6GIvUYZ_h1o",
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      },
      "source": [
        "def read_papers(filename='icml_2020_papers.txt'):\n",
        "    papers = ddict(list)\n",
        "    authors = ddict(list)\n",
        "    affiliations = ddict(set)\n",
        "    author2affiliation = ddict(list)\n",
        "    paper2authors = ddict(set)\n",
        "    paper2affiliations = ddict(set)\n",
        "    with open(filename, encoding=\"utf8\") as f:\n",
        "        while True:\n",
        "            paper_title = next(f)\n",
        "            paper_authors = next(f)\n",
        "\n",
        "            author_splitted = paper_authors.split('·')\n",
        "            paper2authors[paper_title] = author_splitted\n",
        "\n",
        "            for author in author_splitted:\n",
        "                full_name = author[:author.rfind(\"(\")].strip()\n",
        "                affiliation = author[author.rfind(\"(\")+1:author.rfind(\")\")]\n",
        "\n",
        "                paper2affiliations[paper_title].add(affiliation)\n",
        "\n",
        "                papers[paper_title.strip()].append(full_name)\n",
        "                authors[full_name.strip()].append(paper_title)\n",
        "                affiliations[affiliation.strip()].add(paper_title)\n",
        "                author2affiliation[full_name.strip()].append(affiliation)\n",
        "            try:\n",
        "                next(f)\n",
        "            except StopIteration:\n",
        "                return papers, authors, affiliations, author2affiliation, paper2authors, paper2affiliations\n",
        "\n",
        "\n",
        "def plot_bar(top_orgs, title='Number of papers', width=1200, height=800, xname='country', yname='papers',\n",
        "                          xaxis_title=\"\", yaxis_title=\"# papers\", tickangle=45, tickmode='auto', margin_bottom=200,\n",
        "                          save_html=False,\n",
        "                          fontsize=20, title_y=0.98,\n",
        "                          ):\n",
        "    fig = px.bar(top_orgs, x=xname, y=yname, height=height, width=width, text='papers')\n",
        "    fig.update_layout(\n",
        "        title={\n",
        "            'text': title,\n",
        "            'y': title_y,\n",
        "            'x': 0.5,\n",
        "            'xanchor': 'center',\n",
        "            'yanchor': 'top'},\n",
        "        xaxis_title=xaxis_title,\n",
        "        yaxis_title=yaxis_title,\n",
        "        font=dict(\n",
        "            #         family=\"Courier New\",\n",
        "            size=fontsize,\n",
        "            color=\"black\"\n",
        "        ),\n",
        "        margin=dict(b=margin_bottom),\n",
        "        xaxis=dict(tickangle=tickangle,\n",
        "                   tickmode='linear'),\n",
        "\n",
        "    )\n",
        "    fig.update_traces(marker_color='blue')\n",
        "    #     fig.update_traces(textposition='inside')\n",
        "\n",
        "    if save_html:\n",
        "        pio.write_html(fig, file='index.html', auto_open=True)\n",
        "    fig.show()\n",
        "\n",
        "papers, authors, affiliations, author2affiliation, paper2authors, paper2affiliations = read_papers()\n",
        "\n",
        "def read_mapping(filename='university2.csv'):\n",
        "    uni = pd.read_csv(filename)\n",
        "    lowered = [_.lower() for _ in uni.iloc[:, 0].tolist()]\n",
        "    org2name = dict(zip(lowered, uni.iloc[:, 1].tolist()))\n",
        "    org2country = dict(zip(lowered, uni.iloc[:, 2].tolist()))\n",
        "    name2country = dict(zip(uni.iloc[:, 1], uni.iloc[:, 2].tolist()))\n",
        "    return org2name, org2country, name2country\n",
        "\n",
        "org2name, org2country, name2country = read_mapping()\n",
        "\n",
        "def get_papers_for_name_and_country(affiliations, org2name, org2country):\n",
        "    name2papers = ddict(set)\n",
        "    country2papers = ddict(set)\n",
        "    for org in affiliations:\n",
        "        name = org2name.get(org.lower().strip(), org)\n",
        "        country = org2country.get(org.lower().strip(), org)\n",
        "        if name and name not in ['None', 'N/A']:\n",
        "            name2papers[name] = name2papers[name].union(set(affiliations[org]))\n",
        "            country2papers[country] = country2papers[country].union(set(affiliations[org]))\n",
        "    return name2papers, country2papers\n",
        "\n",
        "name2papers, country2papers = get_papers_for_name_and_country(affiliations, org2name, org2country)\n",
        "\n",
        "def get_paper2name(paper2affiliations, org2name):\n",
        "    paper2name = ddict(set)\n",
        "    for paper, affs in paper2affiliations.items():\n",
        "        paper2name[paper] = set([org2name.get(aff.lower().strip(), aff) for aff in affs])\n",
        "    return paper2name\n",
        "\n",
        "paper2name = get_paper2name(paper2affiliations, org2name)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "66omN-O6_h14",
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          "height": 837
        },
        "outputId": "30b5d843-dfe0-4536-d4ef-0a39aa4e0873"
      },
      "source": [
        "# Top authors plot\n",
        "min_papers = 5\n",
        "top_authors = sorted([(len(papers), author + f\" ({author2affiliation[author][0]})\") for author, papers in authors.items() if len(papers) >= min_papers], reverse=True)\n",
        "top_authors = pd.DataFrame(top_authors, columns=['papers', 'author'])\n",
        "\n",
        "plot_bar(top_authors, title=f'Top authors: {min_papers}+ papers', width=1500, height=800, xname='author', yname='papers',\n",
        "                     xaxis_title=\"\", yaxis_title=\"# papers\", tickangle=45, tickmode = 'linear', margin_bottom=400)\n"
      ],
      "execution_count": 4,
      "outputs": [
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          "height": 817
        },
        "outputId": "76e265b1-bb69-4de4-90cd-e980b2b8affc"
      },
      "source": [
        "def get_graph_authors(papers):\n",
        "    graph_papers = [p for p in papers if ('graph' in p.lower()\n",
        "                                          and not 'graphical' in p.lower())\n",
        "                    or ('gnn' in p.lower())]\n",
        "\n",
        "    graph_authors = ddict(list)\n",
        "    for p in graph_papers:\n",
        "        for a in papers[p]:\n",
        "            graph_authors[a].append(p)\n",
        "\n",
        "    return graph_authors\n",
        "\n",
        "graph_authors = get_graph_authors(papers)\n",
        "\n",
        "min_papers = 2\n",
        "top_graph_authors = sorted(\n",
        "    [(len(ps), author + f\" ({author2affiliation[author][0]})\") for author, ps in graph_authors.items() if\n",
        "     len(ps) >= min_papers], reverse=True)\n",
        "top_graph_authors = pd.DataFrame(top_graph_authors, columns=['papers', 'author'])\n",
        "\n",
        "plot_bar(top_graph_authors, title=f'Top graph authors: {min_papers}+ papers', width=1000, height=800, xname='author',\n",
        "         yname='papers',\n",
        "         xaxis_title=\"\", yaxis_title=\"# papers\", tickangle=45, tickmode='linear', margin_bottom=400,\n",
        "         save_html=False)"
      ],
      "execution_count": 5,
      "outputs": [
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        "outputId": "8d50fe87-0d34-491a-ccc8-674b5a49110a"
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      "source": [
        "# Organization per paper\n",
        "most_orgs_papers = sorted(paper2name.items(), key=lambda x: len(x[1]), reverse=True)[:10]\n",
        "most_orgs_papers = pd.DataFrame([[a] + list(b) for a,b in most_orgs_papers])\n",
        "\n",
        "num_orgs2count = ddict(int)\n",
        "for paper, authors in paper2name.items():\n",
        "    num_orgs2count[len(authors)] += 1\n",
        "\n",
        "num_authors_df = pd.DataFrame(list(num_orgs2count.items()), columns=['NumOrgs', 'papers']).sort_values(by='NumOrgs')\n",
        "plot_bar(num_authors_df, title='Number of organizations per paper', height=400, width=1200, xname='NumOrgs', yname='papers',\n",
        "                     xaxis_title=\"Number of organization\", yaxis_title=\"Total papers\", tickangle=0, tickmode = 'linear', margin_bottom=10)\n"
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      "execution_count": 6,
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          "metadata": {
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      "source": [
        "# Authors per paper\n",
        "most_authored_papers = sorted(paper2authors.items(), key=lambda x: len(x[1]), reverse=True)[:3]\n",
        "most_authored_papers = pd.DataFrame([[a] + b for a,b in most_authored_papers])\n",
        "\n",
        "num_authors2count = ddict(int)\n",
        "for paper, authors in paper2authors.items():\n",
        "    num_authors2count[len(authors)] += 1\n",
        "\n",
        "num_authors_df = pd.DataFrame(list(num_authors2count.items()), columns=['NumAuthors', 'papers']).sort_values(by='NumAuthors')\n",
        "plot_bar(num_authors_df, title='Number of authors per paper', height=400, width=1200, xname='NumAuthors', yname='papers',\n",
        "                     xaxis_title=\"Number of authors\", yaxis_title=\"Total papers\", tickangle=0, tickmode = 'linear', margin_bottom=10)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33v_HSeI_h3W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        },
        "outputId": "2126bc16-f635-4514-fb5a-9c14e5634676"
      },
      "source": [
        "# Collaboration plots\n",
        "def get_name2collabs(name2papers):\n",
        "    name2collabs = ddict(list)\n",
        "    for name1, papers1 in name2papers.items():\n",
        "        for name2, papers2 in name2papers.items():\n",
        "            if name1 != name2:\n",
        "                collabs = len(papers1.intersection(papers2))\n",
        "                if collabs:\n",
        "                    name2collabs[name1].append((collabs, name2))\n",
        "    return name2collabs\n",
        "\n",
        "name2collabs = get_name2collabs(name2papers)\n",
        "\n",
        "def plot_graph(data, height=800, title='Collaboration graph', fontsize=30, title_y=0.98):\n",
        "    fig = go.Figure(data=data,\n",
        "             layout=go.Layout(\n",
        "                titlefont_size=fontsize,\n",
        "                showlegend=False,\n",
        "                hovermode='closest',\n",
        "                margin=dict(b=20,l=5,r=5,t=40),\n",
        "#                 annotations=[ dict(\n",
        "#                     text=\"Python code: <a href='https://plotly.com/ipython-notebooks/network-graphs/'> https://plotly.com/ipython-notebooks/network-graphs/</a>\",\n",
        "#                     showarrow=False,\n",
        "#                     xref=\"paper\", yref=\"paper\",\n",
        "#                     x=0.005, y=-0.002 ) ],\n",
        "                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
        "                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n",
        "                )\n",
        "    \n",
        "    fig.update_layout(\n",
        "        title={\n",
        "            'text': title,\n",
        "            'y': title_y,\n",
        "            'x':0.5,\n",
        "            'xanchor': 'center',\n",
        "            'yanchor': 'top',\n",
        "        },\n",
        "        height=height,\n",
        "        font=dict(\n",
        "    #         family=\"Courier New\",\n",
        "            size=fontsize,\n",
        "            color=\"black\"\n",
        "        ),\n",
        "        hoverlabel=dict(\n",
        "#             bgcolor=\"white\", \n",
        "            font_size=30, \n",
        "            font_family=\"Rockwell\"\n",
        "        ),\n",
        "    )\n",
        "    \n",
        "    fig.show()\n",
        "\n",
        "def extract_edge_attributes(G, pos):\n",
        "    middle_node_x = []\n",
        "    middle_node_y = []\n",
        "    middle_node_text = []\n",
        "    edge_traces = []\n",
        "    for edge in G.edges(data=True):\n",
        "        x = [pos[edge[0]][0], pos[edge[1]][0], None]\n",
        "        y = [pos[edge[0]][1], pos[edge[1]][1], None]\n",
        "        width = edge[2]['data']['weight']\n",
        "        edge_traces.append(go.Scatter(\n",
        "                    x=x,\n",
        "                    y=y,\n",
        "                    line=dict(width=width,color='#888'),\n",
        "                    hoverinfo='none',\n",
        "                    mode='lines'))\n",
        "        middle_node_x.append((x[0]+x[1])/2)\n",
        "        middle_node_y.append((y[0]+y[1])/2)\n",
        "        middle_node_text.append(f\"{edge[0]}-{edge[1]}: {edge[2]['data']['weight']}\")\n",
        "    return edge_traces, middle_node_x, middle_node_y, middle_node_text\n",
        "\n",
        "\n",
        "\n",
        "def build_middle_trace(node_x, node_y, node_text):\n",
        "    return go.Scatter(\n",
        "        x=node_x,\n",
        "        y=node_y,\n",
        "        text=node_text,\n",
        "        mode='markers',\n",
        "        hoverinfo='text',\n",
        "        marker=dict(\n",
        "            opacity=0\n",
        "        )\n",
        "    )\n",
        "\n",
        "\n",
        "\n",
        "def extract_node_attributes(G, pos, name2papers):\n",
        "    node_x = []\n",
        "    node_y = []\n",
        "    node_sizes = []\n",
        "    node_texts = []\n",
        "    node_colors = []\n",
        "    for node in G.nodes():\n",
        "        node_x.append(pos[node][0])\n",
        "        node_y.append(pos[node][1])\n",
        "        node_sizes.append(len(name2papers[node]))\n",
        "        node_texts.append(f\"{node}: {len(name2papers[node])}\")\n",
        "        node_colors.append(len(name2papers[node]))\n",
        "    return node_x, node_y, node_sizes, node_texts, node_colors\n",
        "\n",
        "\n",
        "    \n",
        "def build_node_traces(node_x, node_y, node_texts, node_sizes=20, colorbar_title='Number of papers'):\n",
        "    node_trace = go.Scatter(\n",
        "        x=node_x, y=node_y,\n",
        "        mode='markers',\n",
        "        hoverinfo='text',\n",
        "        text=node_texts,\n",
        "        marker=dict(\n",
        "            showscale=True,\n",
        "            # colorscale options\n",
        "            #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |\n",
        "            #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |\n",
        "            #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |\n",
        "            colorscale='YlGnBu',\n",
        "            reversescale=False,\n",
        "            color=node_colors,\n",
        "            size=node_sizes,\n",
        "            colorbar=dict(\n",
        "                thickness=15,\n",
        "                title=colorbar_title,\n",
        "                xanchor='left',\n",
        "                titleside='right'\n",
        "            ),\n",
        "            line_width=2))\n",
        "    \n",
        "    return node_trace\n",
        "\n",
        "num_collab = 30\n",
        "G = nx.Graph()\n",
        "for name, collabs in name2collabs.items():\n",
        "    for num, neighbor in collabs:\n",
        "        G.add_edge(name, neighbor, data={'weight': num})\n",
        "        \n",
        "G = G.subgraph([name for name, d in G.degree if d >= num_collab])\n",
        "pos = nx.kamada_kawai_layout(G)\n",
        "print(len(G), len(G.edges()))\n",
        "\n",
        "edge_traces, middle_node_x, middle_node_y, middle_node_text = extract_edge_attributes(G, pos)\n",
        "middle_node_trace = build_middle_trace(middle_node_x, middle_node_y, middle_node_text)\n",
        "node_x, node_y, node_sizes, node_texts, node_colors = extract_node_attributes(G, pos, name2papers)\n",
        "node_trace = build_node_traces(node_x, node_y, node_texts, node_sizes=node_sizes, colorbar_title='Number of papers')\n",
        "\n",
        "data = edge_traces + [node_trace, middle_node_trace]\n",
        "plot_graph(data, height=800, title=f\"Top collaborators ({num_collab}+ papers collaborated)\", fontsize=40, title_y=1)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10 38\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "77f6cf46-78c8-47b3-9e09-0f65084a9d60"
      },
      "source": [
        "# Organization collaboration\n",
        "for org, min_collab in [('Google', 3), ('DeepMind', 2), ('MIT', 2)]:\n",
        "    collaborations = pd.DataFrame(name2collabs[org], columns=['papers', 'collaborator']).sort_values(by='papers', ascending=False)\n",
        "    plot_bar(collaborations[collaborations.papers >= min_collab], title=f'<b>{org}</b> Collaborations ({min_collab}+ collaboration papers)', height=800, xname='collaborator', yname='papers')\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "# Academy vs Industry\n",
        "def is_uni(s):\n",
        "    lst = ['universit', 'insitut', 'colleg', 'ecole', 'inria', 'eth', 'ut', 'uc', 'kaist', 'mila', 'kaust', 'riken', 'iit', 'umass', 'mit']\n",
        "    for sub in lst:\n",
        "        if sub in s.lower():\n",
        "            return True\n",
        "    return False\n",
        "\n",
        "name2papers, country2papers = get_papers_for_name_and_country(affiliations, org2name, org2country)\n",
        "\n",
        "top_orgs = sorted([(len(ps), org) for org, ps in name2papers.items()], reverse=True)\n",
        "top_orgs = pd.DataFrame(top_orgs, columns=['papers', 'name'])\n",
        "top_orgs = top_orgs[top_orgs.papers > 3]\n",
        "top_orgs['is_uni'] = top_orgs.name.apply(is_uni)\n",
        "\n",
        "def get_countries(org2papers, org2country, country_name=None):\n",
        "    countries = []\n",
        "    for org in org2papers:\n",
        "        \n",
        "        oc = org2country.get(org, org)\n",
        "        if country_name:\n",
        "            if oc == country_name:\n",
        "                countries.append(oc)\n",
        "        else:\n",
        "            countries.append(oc)\n",
        "    return countries\n",
        "\n",
        "top_orgs['country'] = get_countries(top_orgs.name.tolist(), name2country, None)\n",
        "top_orgs.loc[top_orgs.name == 'Criteo', 'papers'] = 9 # setting up real number due to missing affiliation\n",
        "top_orgs = top_orgs.sort_values(by='papers', ascending=False)\n",
        "\n",
        "industry_names = top_orgs[~top_orgs.is_uni]\n",
        "plot_bar(industry_names, title='<b>Industry</b>: Number of papers', height=600, width=1200, xname='name', yname='papers', margin_bottom=20, fontsize=30, title_y=0.98)\n",
        "\n",
        "academy_names = top_orgs[top_orgs.is_uni]\n",
        "plot_bar(academy_names[:20], title='<b>Academy</b>: Number of papers', height=1000, width=1500, xname='name', yname='papers', margin_bottom=200, fontsize=30, title_y=0.98)\n",
        "\n",
        "industry = set()\n",
        "academy = set()\n",
        "unis = top_orgs[top_orgs.is_uni == True].name.tolist()\n",
        "inds = top_orgs[top_orgs.is_uni == False].name.tolist()\n",
        "for uni in unis:\n",
        "    academy = academy.union(name2papers[uni])\n",
        "    \n",
        "for ind in inds:\n",
        "     industry = industry.union(name2papers[ind])\n",
        "\n",
        "ind_vs_acad = pd.DataFrame([('Academy', len(academy)), ('Industry', len(industry))], columns=['country', 'papers'])\n",
        "plot_bar(ind_vs_acad, title=f\"Industry vs Academy\",\n",
        "                     height=400, width=600, margin_bottom=20, fontsize=35, tickangle=0)\n"
      ],
      "execution_count": 16,
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    {
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        "colab_type": "code",
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      "source": [
        "name2papers, country2papers = get_papers_for_name_and_country(affiliations, org2name, org2country)\n",
        "\n",
        "country_filtered = ddict(set)\n",
        "for name in name2papers:\n",
        "  \n",
        "  if is_uni(name):\n",
        "    country = name2country.get(name, name)\n",
        "    country_filtered[country] = country_filtered[country].union(set(name2papers[name]))\n",
        "\n",
        "country_filtered = sorted([(len(ps), country) for country, ps in country_filtered.items()], reverse=True)\n",
        "country_filtered = pd.DataFrame(country_filtered, columns=['papers', 'country'])\n",
        "plot_bar(country_filtered.head(27), title='<b>Universities</b>: Number of papers by country', height=600, width=1200, xname='country', yname='papers', margin_bottom=200, fontsize=30, title_y=0.98)"
      ],
      "execution_count": 31,
      "outputs": [
        {
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      "metadata": {
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        "colab_type": "code",
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        "outputId": "0144eac5-a7f5-40ac-d52c-8b63cdc9c2b4"
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      "source": [
        "# Top organizations\n",
        "top = 30\n",
        "top_orgs = sorted([(len(ps), org) for org, ps in name2papers.items()], reverse=True)[:top]\n",
        "top_orgs = pd.DataFrame(top_orgs, columns=['papers', 'country'])\n",
        "\n",
        "plot_bar(top_orgs, title=f\"<b>Global</b>: Number of papers by organization\",\n",
        "                     height=800, width=1500, fontsize=25, margin_bottom=300)\n",
        "\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
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        "id": "WI4ASWIJ_h4J",
        "colab_type": "code",
        "colab": {
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        "outputId": "e8ffd085-123a-49cd-bd58-af9dde79ab97"
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      "source": [
        "# Top organizations by country\n",
        "by_country_data = sorted([(len(p), c) for c, p in country2papers.items()], reverse=True)[:29]\n",
        "by_country_data = pd.DataFrame(by_country_data, columns=['papers', 'country'])\n",
        "plot_bar(by_country_data, \"Number of papers by country\", height=600, width=1500, fontsize=30)\n",
        "\n",
        "top_countries = by_country_data.country[:15].tolist()\n",
        "for country_name, top in zip(top_countries, [10,9,10,10,7,6,2,5,3,2,4,5,2,3,1]):\n",
        "\n",
        "    names_in_country = [name for name, country in name2country.items() if country == country_name]\n",
        "\n",
        "    top_orgs = sorted([(len(name2papers[name]), str(name)) for name in names_in_country], reverse=True)[:top]\n",
        "    top_orgs = pd.DataFrame(top_orgs, columns=['papers', 'country'])\n",
        "    top_orgs.loc[top_orgs.country == 'Criteo', 'papers'] = 9 # setting up real number due to missing affiliation\n",
        "    top_orgs = top_orgs.sort_values(by='papers', ascending=False) # setting up real number due to missing affiliation\n",
        "\n",
        "    plot_bar(top_orgs, title=f\"<b>{country_name}</b>: Number of papers by organization\",\n",
        "                         height=800, fontsize=30, margin_bottom=400)"
      ],
      "execution_count": 12,
      "outputs": [
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